{"id":59531,"date":"2025-05-03T21:48:50","date_gmt":"2025-05-03T13:48:50","guid":{"rendered":"https:\/\/swarma.org\/?p=59531"},"modified":"2025-05-03T21:48:50","modified_gmt":"2025-05-03T13:48:50","slug":"nature%e7%bb%bc%e8%bf%b0%ef%bc%9a%e5%a4%a7%e8%a7%84%e6%a8%a1%e7%a5%9e%e7%bb%8f%e5%bd%a2%e6%80%81%e8%ae%a1%e7%ae%97","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=59531","title":{"rendered":"Nature\u7efc\u8ff0\uff1a\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97"},"content":{"rendered":"
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\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\uff08Neuromorphic Computing\uff09\u6307\u8111\u542f\u53d1\u7684\u786c\u4ef6\u4e0e\u7b97\u6cd5\u8bbe\u8ba1\u65b9\u6cd5\uff0c\u7814\u7a76\u8005\u4eec\u501f\u9274\u795e\u7ecf\u79d1\u5b66\u4e2d\u7684\u751f\u7269\u667a\u80fd\u539f\u7406\u6765\u8bbe\u8ba1\u9ad8\u6548\u7684\u8ba1\u7b97\u7cfb\u7edf\uff0c\u5c24\u5176\u9002\u7528\u4e8e\u5bf9\u4f53\u79ef\u3001\u91cd\u91cf\u548c\u529f\u8017\u6709\u4e25\u683c\u8981\u6c42\u7684\u5e94\u7528\u573a\u666f\u3002\u5f53\u524d\uff0c\u8be5\u7814\u7a76\u9886\u57df\u6b63\u5904\u4e8e\u53d1\u5c55\u7684\u5173\u952e\u9636\u6bb5\uff0c\u56e0\u6b64\u660e\u786e\u672a\u6765\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u53d1\u5c55\u65b9\u5411\u81f3\u5173\u91cd\u8981\u3002\u672c\u6587\u63a2\u8ba8\u4e86\u6784\u5efa\u89c4\u6a21\u53ef\u6269\u5c55\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u67b6\u6784\u7684\u65b9\u6cd5\uff0c\u5e76\u603b\u7ed3\u4e86\u5176\u4e2d\u7684\u5173\u952e\u7279\u5f81\u3002\u6b64\u5916\uff0c\u6211\u4eec\u5206\u6790\u4e86\u53ef\u4ee5\u4ece\u89c4\u6a21\u6269\u5c55\u4e2d\u83b7\u76ca\u7684\u6f5c\u5728\u5e94\u7528\u573a\u666f\uff0c\u4ee5\u53ca\u9700\u8981\u89e3\u51b3\u7684\u4e3b\u8981\u6311\u6218\u3002\u8fdb\u4e00\u6b65\u5730\uff0c\u6211\u4eec\u5ba1\u89c6\u4e86\u652f\u6301\u8be5\u9886\u57df\u6301\u7eed\u53d1\u5c55\u7684\u5b8c\u6574\u6280\u672f\u751f\u6001\u7cfb\u7edf\uff0c\u5e76\u63a2\u8ba8\u4e86\u89c4\u6a21\u6269\u5c55\u6240\u5e26\u6765\u7684\u65b0\u673a\u9047\u3002\u6211\u4eec\u7684\u7814\u7a76\u6c47\u603b\u4e86\u591a\u4e2a\u8ba1\u7b97\u5b50\u9886\u57df\u7684\u89c2\u70b9\uff0c\u4e3a\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7814\u7a76\u4eba\u5458\u548c\u4ece\u4e1a\u8005\u63d0\u4f9b\u6307\u5bfc\uff0c\u4ee5\u63a8\u52a8\u8be5\u9886\u57df\u7684\u8fdb\u4e00\u6b65\u53d1\u5c55\u3002<\/span><\/span><\/strong><\/span><\/strong><\/p>\n


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\u7814\u7a76\u9886\u57df\uff1a<\/span>\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\uff0c\u8111\u542f\u53d1\uff0c\u751f\u7269\u667a\u80fd<\/span><\/strong><\/span><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/span><\/strong><\/strong><\/strong><\/p>\n<\/section>\n

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Dhireesha Kudithipudi<\/span><\/span>, Catherine Schuman, Craig M. Vineyard<\/span><\/span>\u7b49<\/span>\u4e28\u4f5c\u8005<\/span><\/strong><\/span><\/span><\/p>\n

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\u8bba\u6587\u9898\u76ee\uff1aNeuromorphic computing at scale<\/span><\/span><\/p>\n

\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/www.nature.com\/articles\/s41586-024-08253-8<\/span><\/span><\/section>\n<\/blockquote>\n

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\u968f\u7740\u795e\u7ecf\u7f51\u7edc\u5728\u8d8a\u6765\u8d8a\u591a\u7684\u5e94\u7528\u9886\u57df\u4ea7\u751f\u5e7f\u6cdb\u5f71\u54cd\uff0c\u4eba\u7c7b\u5927\u8111\u4ecd\u7136\u662f\u7814\u7a76\u8005\u4eec\u8fdb\u4e00\u6b65\u6a21\u62df\u590d\u6742\u8ba1\u7b97\u80fd\u529b\u5e76\u5b9e\u73b0\u7a81\u7834\u7684\u91cd\u8981\u7075\u611f\u6765\u6e90\u3002\u7136\u800c\uff0c\u8981\u5b9e\u73b0\u53d7\u5927\u8111\u542f\u53d1\u7684\u673a\u5668\u667a\u80fd\u9700\u8981\u5728\u8ba1\u7b97\u5e73\u53f0\u7684\u8bbe\u8ba1\u4e0e\u6784\u5efa\u65b9\u5f0f\u4e0a\u8fdb\u884c\u6839\u672c\u6027\u7684\u53d8\u9769\u3002\u5728\u8fd9\u4e00\u65b9\u5411\u4e0a\uff0c\u6700\u5177\u524d\u666f\u7684\u7814\u7a76\u4e4b\u4e00\u662f\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97<\/span><\/span>\uff08Neuromorphic Computing\uff09<\/span><\/span>\uff0c\u5373\u53d7\u5927\u8111\u542f\u53d1\u7684\u786c\u4ef6\u4e0e\u7b97\u6cd5\u8bbe\u8ba1\u65b9\u6cd5\uff0c\u80fd\u591f\u9ad8\u6548\u5b9e\u73b0\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc[<\/span><\/span>1]<\/span><\/span>\u3002\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u9886\u57df\u7814\u7a76\u8005\u501f\u9274\u795e\u7ecf\u79d1\u5b66\u63d0\u51fa\u7684\u751f\u7269\u667a\u80fd<\/span><\/span>\uff08Biointelligence\uff09<\/span><\/span>\u539f\u7406\u6765\u8bbe\u8ba1\u9ad8\u6548\u7684\u8ba1\u7b97\u7cfb\u7edf\uff0c\u5c24\u5176\u9002\u7528\u4e8e\u5bf9\u4f53\u79ef\u3001\u91cd\u91cf\u548c\u529f\u8017\u6709\u4e25\u683c\u9650\u5236\u7684\u5e94\u7528\u573a\u666f\u3002<\/span><\/span><\/section>\n

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\u6839\u636e\u8fd1\u5e74\u6765\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u539f\u578b<\/span><\/span>\uff08prototype\uff09<\/span><\/span>\u7cfb\u7edf\u7684\u53d1\u5c55\u901f<\/span><\/span>\u5ea6\u8fdb\u884c\u63a8\u6d4b\uff0c\u8be5\u9886\u57df\u5728\u672a\u6765\u4eba\u5de5\u667a\u80fd<\/span><\/span>\uff08artificial intelligence\uff09<\/span><\/span>\u5e94\u7528\u4e2d\u5177\u6709\u5de8\u5927\u7684\u6f5c\u529b\u3002\u9884\u8ba1\u52302026\u5e74\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u82af\u7247\u7684\u5e02\u573a\u89c4\u6a21\u5c06\u8fbe\u52305.566\u4ebf\u7f8e\u5143<\/span><\/span>[2]\u3002\u4e00\u4e9b\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u82af\u7247\u6b63\u5feb\u901f\u8fdb\u5165\u65e9\u671f\u5546\u4e1a\u5e02\u573a\uff0c\u5e76\u5df2\u5c55\u73b0\u51fa\u5728\u4e0d\u540c\u89c4\u6a21\u7684\u8ba1\u7b97\u4efb\u52a1\u4e2d\u5b9e\u73b0\u6781\u4f4e\u529f\u8017\u548c\u4f4e\u5ef6\u8fdf\u6027\u80fd\u7684\u6f5c\u529b[3,4]\u3002\u8fd9\u4e00\u9886\u57df\u8fc5\u901f\u53d1\u5c55\u7684\u539f<\/span><\/span>\u56e0\u4e4b\u4e00\u5728\u4e8e\u5176\u7cfb\u7edf\u7684\u591a\u529f\u80fd\u6027\u3002\u4f8b\u5982\uff0c\u4f20\u7edf\u8ba1\u7b97\u7684\u8fdb\u6b65\u901a\u5e38\u4e13\u6ce8\u4e8e\u7279\u5b9a\u7c7b\u578b\u7684\u8ba1\u7b97\u67b6\u6784\u2014\u2014\u8d85\u7b97\u9886\u57df\u805a\u7126\u4e8e\u767e\u4ebf\u4ebf\u6b21<\/span><\/span>\uff08Exascale\uff09<\/span><\/span>\u8ba1\u7b97\uff0c\u800c\u5d4c\u5165\u5f0f\u7cfb\u7edf\u5219\u5f3a\u8c03\u5c0f\u578b\u5316\uff0c\u8fd9\u4e24\u79cd\u67b6\u6784\u901a\u5e38\u4e0d\u4f1a\u76f8\u4e92\u501f\u9274\uff0c\u800c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5219\u80fd\u591f\u901a\u8fc7\u7edf\u4e00\u7684\u8ba1\u7b97\u6280\u672f\u5728\u8fd9\u4e24\u7c7b\u8ba1\u7b97\u67b6\u6784\u4e2d\u540c\u65f6\u5e26\u6765\u53d8\u9769\uff0c\u5177\u5907\u98a0\u8986\u6027\u7684\u6f5c\u529b\u3002\u5f53\u524d\u8be5\u9886\u57df\u9762\u4e34\u7684\u5173\u952e\u95ee\u9898\u5728\u4e8e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u662f\u5426\u5df2\u7ecf\u51c6\u5907\u597d\u5b9e\u73b0\u771f\u6b63\u7684\u8ba1\u7b97\u7a81\u7834\uff0c\u7c7b\u4f3c\u4e8e\u201cAlexNet\u65f6\u523b\u201d<\/span><\/span>\uff08\u89c1\u68461\uff09<\/span><\/span>\u3002\u6b64\u5916<\/span><\/span>\uff0c\u8bc4\u4f30\u67d0\u79cd\u65b9\u6cd5\u7684\u6210\u719f\u5ea6\u8fdc\u6bd4\u8ba1\u7b97\u5355\u4e00\u7684\u6027\u80fd\u6307\u6807\u66f4\u52a0\u590d\u6742\uff0c\u89c4\u6a21\u5316\u662f\u8861\u91cf\u8be5\u9886\u57df\u8fdb\u5c55\u7684\u91cd\u8981\u7ef4\u5ea6\u4e4b\u4e00\u3002\u5982\u4eca\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u6b63\u5904\u4e8e\u5173\u952e\u53d1\u5c55\u9636\u6bb5\uff0c\u6211\u4eec\u7684\u76ee\u6807\u662f\u8bc6\u522b\u5e76\u89e3\u51b3\u5173\u952e\u9700\u6c42\uff0c\u4ece\u800c\u63a8\u52a8\u53d8\u9769\u6027\u5f71\u54cd\u7684\u4ea7\u751f\u3002<\/span><\/span><\/section>\n

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    \u5728\u56fe<\/span><\/span>1<\/span><\/span>\u4e2d\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u53d1\u5c55\u65f6\u95f4\u7ebf\uff0c\u6db5\u76d6\u4e86\u8be5\u9886\u57df\u81f3\u4eca\u6240\u53d6\u5f97\u7684\u5173\u952e\u6280\u672f\u8fdb\u5c55\u3002\u8fd9\u4e9b\u7cfb\u7edf\u7684\u5e95\u5c42\u67b6\u6784\u6807\u5fd7\u7740\u8ba1\u7b97\u5728\u590d\u6742\u6027\u3001\u901a\u7528\u6027\u548c\u5f02\u6784\u6027\u65b9\u9762\u7684\u91cd\u8981\u91cc\u7a0b\u7891\u3002\u7136\u800c\uff0c\u5728\u8ba1\u7b97\u5806\u6808\u7684\u6bcf\u4e2a\u5c42\u7ea7\u4ecd\u7136\u5b58\u5728\u8bf8\u591a\u6311\u6218\uff0c\u5fc5\u987b\u52a0\u4ee5\u89e3\u51b3\uff0c\u624d\u80fd\u4f7f\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u673a\u771f\u6b63\u8d70\u5411\u5b9e\u7528\u5316\u5e76\u88ab\u5e7f\u6cdb\u5e94\u7528\u3002<\/span><\/span><\/section>\n

    <\/span><\/span><\/section>\n
    <\/span><\/span><\/section>\n

    \u56fe1\uff1a\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u53d1\u5c55\u8fdb\u7a0b<\/span><\/span><\/h4>\n

    <\/span><\/section>\n
    \u56fe1\u5c55\u793a\u4e86\u795e\u7ecf\u5143\u6570\u91cf<\/span><\/span>\uff08y\u8f74\uff09<\/span><\/span>\u3001\u82af\u7247\u4e0e\u8ba1\u7b97\u677f\u7684\u89c4\u6a21\u968f\u65f6\u95f4<\/span><\/span>\uff08x\u8f74\uff09<\/span><\/span>\u589e\u957f\u7684\u8d8b\u52bf\u3002\u5c3d\u7ba1\u8fd9\u4ec5\u4ee3\u8868\u89c4\u6a21\u6269\u5c55\u7684\u4e00\u4e2a\u7ef4\u5ea6\uff0c\u4f46\u5728\u8fd9\u4e00\u8fc7\u7a0b\u4e2d\uff0c\u8ba1\u7b97\u67b6\u6784\u548c\u901a\u4fe1\u6a21\u578b\u4e5f\u5728\u4e0d\u65ad\u6f14\u8fdb\u4ee5\u652f\u6301\u7cfb\u7edf\u7684\u6269\u5c55\u3002\u968f\u7740\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u53d1\u5c55\uff0c\u5bf9\u89c4\u6a21\u5316\u80fd\u529b\u7684\u63d0\u5347\u9700\u8981\u7efc\u5408\u8003\u8651<\/span><\/span>\u67b6\u6784\u8bbe\u8ba1\u3001\u90e8\u7f72\u7b56\u7565\u548c\u5de5\u5177\u94fe\u7b49\u591a\u4e2a\u65b9\u9762\u3002\u4e3a\u7b80\u6d01\u8d77\u89c1\uff0c\u56fe\u4e2d\u4ec5\u5c55\u793a\u4e86\u90e8\u5206\u516c\u5f00\u8bb0\u5f55\u7684\u795e\u7ecf\u5f62\u6001\u52a0\u901f\u5668[3,37,39,40,41,49,102,109,110,111,112,113,114,115,116,117,118,119,120,121]\u3002\u76ee\u524d\uff0c\u90e8\u5206\u7cfb\u7edf<\/span><\/span>\uff08\u5982DeepSouth\uff09<\/span><\/span>\u91c7\u7528\u73b0\u6210\u82af\u7247<\/span><\/span>\uff08\u4f8b\u5982\u73b0\u573a\u53ef\u7f16\u7a0b\u95e8\u9635\u5217\uff08FPGA, Field-Programmable Gate Array\uff09\uff09<\/span><\/span>\u4ee5\u5b9e\u73b0\u89c4\u6a21\u5316\u6269\u5c55\u3002\u800c\u5176\u4ed6\u67b6\u6784<\/span><\/span>\uff08\u5982IBM NorthPole122\uff09<\/span><\/span>\u5219\u63a2\u7d22\u901a\u8fc7\u7c7b\u795e\u7ecf\u529f\u80fd\u52a0\u901f\u795e\u7ecf\u7f51\u7edc\u8ba1\u7b97\uff0c\u5176\u4e2d\u795e\u7ecf\u5143\u5e76\u975e\u6838\u5fc3\u8ba1\u7b97\u5355\u5143\uff0c\u56e0\u6b64\u8fd9\u4e9b\u67b6\u6784\u7684\u8fdb\u5c55\u672a\u7eb3\u5165\u56fe\u4e2d\u3002\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u9886\u57df\u6b63\u5feb\u901f\u53d1\u5c55\uff0c\u9884\u8ba12026\u5e74\u5c06\u51fa\u73b0\u62e5\u6709\u8d85\u8fc7100\u4ebf\u4e2a\u795e\u7ecf\u5143\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u3002<\/span><\/span><\/section>\n

    <\/span><\/span><\/section>\n
    \u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63a2\u8ba8\u4e86\u53ef\u6269\u5c55\u4e14\u5b9e\u7528\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u57fa\u7840\u8bbe\u65bd[5]\u7684\u672c<\/span><\/span>\u8d28\u3001\u9700\u6c42\u3001\u91cd\u8981\u6027\u53ca\u9762\u4e34\u7684\u6311\u6218\u3002\u5177\u4f53\u800c\u8a00\uff0c\u6211\u4eec\u5206\u6790\u4e86\u89c4\u6a21\u5316\u8ba1\u7b97\u7684\u5173\u952e\u7279\u5f81\uff0c\u5e76\u8ba8\u8bba\u5982\u4f55\u4f7f\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u57fa\u7840\u8bbe\u65bd\u66f4\u5e7f\u6cdb\u5730\u670d\u52a1\u4e8e\u4e0d\u540c\u9886\u57df\u7684\u7814\u7a76\u4eba\u5458\u548c\u4ece\u4e1a\u8005\u3002\u6b64\u5916\uff0c\u6211\u4eec\u6df1\u5165\u7814\u7a76\u4e86\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u8ba1\u7b97\u5806\u6808\u53ca\u5de5\u5177\u94fe\uff0c\u8bc6\u522b\u4e86\u5176\u4e2d\u7684\u5173\u952e\u6311\u6218\u4e0e\u6f5c\u5728\u673a\u9047\u3002\u6211\u4eec\u671f\u5f85\u8fd9\u4e9b\u89c2\u70b9\u80fd\u591f\u6fc0\u53d1\u65b0\u7684\u601d\u8003\u548c\u8de8\u5b66\u79d1\u5408\u4f5c\uff0c\u4ece\u800c\u52a0\u901f\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u53d1\u5c55\u3002<\/span><\/span><\/section>\n

    <\/span><\/span><\/section>\n
    \n
    \n
    \n
    \n
    \n
    \n

    <\/span><\/section>\n<\/section>\n
    \n
    \n
    \n
    \n

    \u68461\uff1a\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u201cAlexNet\u65f6\u523b\u201d<\/span><\/span><\/p>\n


    <\/span><\/span><\/p>\n

    \u5728\u63a2\u7d22\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u6f5c\u529b\u65f6\uff0c\u6211\u4eec\u53ef\u4ee5\u4ece\u6df1\u5ea6\u5b66\u4e60\u9769\u547d\u7684\u6f14\u8fdb\u5386\u7a0b\u4e2d\u83b7\u5f97\u542f\u53d1\u3002\u4e24\u8005\u90fd\u5efa\u7acb\u5728\u4e0d\u540c\u5c42\u6b21\u7684\u795e\u7ecf\u7f51\u7edc\u7406\u89e3\u4e4b\u4e0a\uff0c\u5e76\u7ecf\u5386\u4e86\u957f\u671f\u7684\u6280\u672f\u74f6\u9888\u671f\u3002\u7c7b\u4f3c\u4e8e\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN, Convolutional Neural Network\uff09\u5728\u624b\u5199\u6570\u5b57\u8bc6\u522b[140]\u65b9\u9762\u7684\u6210\u719f\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u6b63\u5904\u4e8e\u5373\u5c06\u8fce\u6765\u201cAlexNet\u65f6\u523b\u201d\u7684\u9636\u6bb5\u2014\u2014\u4e00\u4e2a\u80fd\u591f\u7a81\u7834\u73b0\u6709\u5c40\u9650\u3001\u5145\u5206\u5c55\u73b0\u5176\u6f5c\u529b\u7684\u5173\u952e\u8f6c\u6298\u70b9[141]\u3002\u672c\u6587\u603b\u7ed3\u4e86\u5f53\u524d\u4e9f\u5f85\u89e3\u51b3\u7684\u95ee\u9898\uff0c\u8fd9\u4e9b\u95ee\u9898\u7684\u7a81\u7834\u5c06\u52a0\u901f\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u53d1\u5c55\uff0c\u4f7f\u5176\u5f71\u54cd\u529b\u53ef\u4e0e\u6df1\u5ea6\u5b66\u4e60\u76f8\u5ab2\u7f8e\u3002<\/span><\/span><\/p>\n


    <\/span><\/span><\/p>\n

    \u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u8fdb\u5c55\u53ef\u80fd\u53d6\u51b3\u4e8e\u4e13\u7528\u786c\u4ef6\uff0c\u6b63\u5982AlexNet\uff08\u4e00\u79cd\u7ecf\u5178\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff0c\u7531 Alex Krizhevsky\u3001Ilya Sutskever \u548c Geoffrey Hinton \u5728 2012 \u5e74 ImageNet \u56fe\u50cf\u5206\u7c7b\u7ade\u8d5b\u4e2d\u63d0\u51fa\uff0c\u5c06\u56fe\u50cf\u5206\u7c7b\u7684\u6b63\u786e\u7387\u63d0\u5347\u5230\u524d\u6240\u672a\u6709\u7684\u9ad8\u5ea6\uff09\u7684\u6210\u529f\u4f9d\u8d56\u4e8e\u901a\u7528\u56fe\u5f62\u5904\u7406\u5355\u5143\uff08GPU\uff09\u7684\u6027\u80fd\u63d0\u5347\u3002AlexNet\u7684\u51fa\u73b0\u5f15\u53d1\u4e86\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u89c4\u6a21\u5316\u6d6a\u6f6e\uff0c\u5e26\u6765\u4e86\u66f4\u5f3a\u7684\u8ba1\u7b97\u8bbe\u5907[142]\u3001\u66f4\u5927\u89c4\u6a21\u7684\u795e\u7ecf\u7f51\u7edc[143]\u4ee5\u53ca\u663e\u8457\u63d0\u5347\u7684\u82af\u7247\u6027\u80fd[144]\u3002\u7c7b\u6bd4\u800c\u8a00\uff0c\u4e3a\u4e86\u91ca\u653e\u5176\u6f5c\u529b\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u9700\u8981\u786e\u5b9a\u5176\u5173\u952e\u7684\u786c\u4ef6\u9700\u6c42\u3002\u867d\u7136\u5f53\u524d\u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u7cfb\u7edf\u4f9d\u8d56\u6570\u5343\u9897\u52a0\u901f\u82af\u7247\uff0c\u4f46\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u7a81\u7834\u53ef\u80fd\u6765\u81ea\u4e00\u4e2a\u5c0f\u89c4\u6a21\u786c\u4ef6\u914d\u7f6e\u2014\u2014\u6bd5\u7adf\uff0cAlexNet\u6700\u521d\u4ec5\u4f7f\u7528\u4e86\u4e24\u5757GPU\u3002\u5982\u679c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u80fd\u591f\u5728\u5c0f\u89c4\u6a21\u7cfb\u7edf\u4e0a\u5b9e\u73b0\u7a81\u7834\uff0c\u5219\u5c06\u6fc0\u53d1\u66f4\u5927\u89c4\u6a21\u7cfb\u7edf\u7684\u7814\u53d1\uff0c\u8fdb\u800c\u91cd\u590d\u6df1\u5ea6\u5b66\u4e60\u7684\u6210\u529f\u8def\u5f84\u3002\u56e0\u6b64\uff0c\u5728\u5c0f\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u4e2d\u6d8c\u73b0\u7684\u201cAlexNet\u65f6\u523b\u201d\uff0c\u6709\u671b\u4e3a\u672a\u6765\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u843d\u5730\u5e94\u7528\u5960\u5b9a\u57fa\u7840\u3002<\/span><\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/span><\/section>\n<\/section>\n<\/section>\n


    <\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n

    \n
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    <\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n

    \n
    \n
    \n
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    <\/span><\/section>\n

    <\/span><\/section>\n
    \n
    <\/section>\n<\/section>\n<\/section>\n
    \n

    \u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u89c4\u6a21\u5316\u53d1\u5c55<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

    \n

    <\/span><\/section>\n

    <\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n


    <\/span><\/p>\n

    \u6211\u4eec\u5c06\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5b9a\u4e49\u4e3a\u4e00\u4e2a\u5305\u62ec\u7b97\u6cd5\u3001\u786c\u4ef6\u3001\u67b6\u6784\u548c\u57fa\u7840\u8bbe\u65bd\u7684\u8ba1\u7b97\u7cfb\u7edf\uff0c\u5176\u5177\u5907\u5728\u89c4\u6a21\u3001\u901f\u5ea6\u548c\u80fd\u8017\u7b49\u65b9\u9762\u6ee1\u8db3\u590d\u6742\u73b0\u5b9e\u4efb\u52a1\u9700\u6c42\u7684\u80fd\u529b\u3002\u8fd9\u4e00\u76ee\u6807\u53ef\u4ee5\u901a\u8fc7\u5728\u6570\u636e\u4e2d\u5fc3\u4e2d\u5229\u7528\u591a\u4e2a\u5927\u89c4\u6a21\u7cfb\u7edf\u8fdb\u884c\u865a\u62df\u5316\u8bbf\u95ee\uff0c\u901a\u8fc7\u8fb9\u7f18\u8bbe\u5907\u6784\u6210\u7684\u5927\u578b\u7f51\u7edc\u5b9e\u73b0\u5206\u5e03\u5f0f\u667a\u80fd\uff0c\u6216\u4e24\u8005\u76f8\u7ed3\u5408\u3002\u8981\u63a8\u52a8\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u89c4\u6a21\u5316\u53d1\u5c55\uff0c\u5fc5\u987b\u8d85\u8d8a\u5b9e\u9a8c\u5ba4\u4e2d\u7684\u6982\u5ff5\u9a8c\u8bc1\uff0c\u5b9e\u73b0\u9762\u5411\u73b0\u5b9e\u4efb\u52a1\u7684\u89c4\u6a21\u5316\u90e8\u7f72\u3002\u4e00\u65e6\u89c4\u6a21\u5316\u843d\u5730\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u53ef\u80fd\u4f1a\u88ab\u6570\u767e\u4e07\u7528\u6237\u6240\u91c7\u7528\uff0c\u65e0\u8bba\u4ed6\u4eec\u662f\u5426\u610f\u8bc6\u5230\u5176\u5b58\u5728\u3002\u56e0\u6b64\u89c4\u6a21\u5316\u843d\u5730\u80fd\u591f\u6807\u5fd7\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u4ece\u4f20\u7edf\u7814\u7a76\u8303\u5f0f\u5411\u5b9e\u9645\u5e94\u7528\u7684\u91cd\u5927\u8f6c\u53d8\u3002\u9ad8\u6027\u80fd\u8ba1\u7b97<\/span><\/span>\uff08HPC\uff09<\/span><\/span>\u9886\u57df\u4e2d\u8fd1\u671f\u795e\u7ecf\u5f62\u6001\u5e94\u7528\u7684\u589e\u957f\u53ef\u4ee5\u63d0\u4f9b\u521d\u6b65\u793a\u8303\u3002\u5927\u591a\u6570\u79d1\u5b66\u8ba1\u7b97\u7684\u9ad8\u6027\u80fd\u8ba1\u7b97\u7cfb\u7edf\u662f\u57fa\u4e8e\u8ba1\u7b97\u8d44\u6e90\uff0c\u53ef\u4ee5\u6839\u636e\u4e0d\u540c\u79d1\u5b66\u4efb\u52a1\u7684\u9700\u6c42\u8fdb\u884c\u8d44\u6e90\u6269\u5c55\u3002\u5c3d\u7ba1\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5728\u672c\u8d28\u4e0a\u4e0d\u540c\u4e8e\u51af\u00b7\u8bfa\u4f9d\u66fc<\/span><\/span>\uff08Von Neumann\uff09<\/span><\/span>\u67b6\u6784\uff0c\u4f46\u5176\u901a\u7528\u8ba1\u7b97\u67b6\u6784\u6216\u8bb8\u975e\u5e38\u9002\u5408HPC\u573a\u666f\u4e2d\u7684\u6309\u9700\u8ba1\u7b97\u8d44\u6e90\uff1a\u540c\u4e00\u5957\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u65e2\u53ef\u4ee5\u7528\u4e8e\u5927\u89c4\u6a21\u79d1\u5b66\u8ba1\u7b97\u6a21\u62df\uff0c\u4e5f\u53ef\u4ee5\u652f\u6301\u5c0f\u578b\u5316\u7684\u8fb9\u7f18\u8ba1\u7b97\u548c\u5206\u5e03\u5f0f\u667a\u80fd\u5e94\u7528\u3002<\/span><\/span><\/section>\n

    <\/span><\/span><\/section>\n
    \u968f\u7740\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u53d1\u5c55\uff0c\u89c4\u6a21\u5316\u80fd\u529b\u7684\u63d0\u5347\u9700\u8981\u7efc\u5408\u8003\u8651\u5f00\u53d1\u3001\u90e8\u7f72\u548c\u5de5\u5177\u94fe\u7b49\u591a\u4e2a\u65b9\u9762\u3002\u76ee\u524d\uff0c\u867d\u7136\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u7814\u7a76\u6b63\u4ee5\u6781\u5feb\u7684\u901f\u5ea6\u63a8\u8fdb\uff0c\u4f46\u76f4\u5230\u6700\u8fd1\u624d\u5f00\u59cb\u4ea7\u751f\u8f83\u5927\u5f71\u54cd\u3002\u5c3d\u7ba1\u672a\u6765\u7684\u5177\u4f53\u53d1\u5c55\u96be\u4ee5\u9884\u6d4b\uff0c\u4f46\u6211\u4eec\u9884\u8ba1\u5176\u5e26\u6765\u7684\u53d8\u9769\u6027\u5f71\u54cd\u4e0d\u4f1a\u4e9a\u4e8e\u4eba\u5de5\u667a\u80fd\u9769\u547d\u3002<\/span><\/span><\/section>\n

    <\/span><\/span><\/section>\n
    \u957f\u671f\u4ee5\u6765\uff0c\u5fae\u7535\u5b50\u4ea7\u4e1a\u4e00\u76f4\u4ee5\u201c\u89c4\u6a21\u201d\u4f5c\u4e3a\u521b\u65b0\u8fdb\u5c55\u7684\u6838\u5fc3\u5ea6\u91cf\u6807\u51c6\u3002\u4f8b\u5982\uff1a\u6469\u5c14\u5b9a\u5f8b<\/span><\/span>\uff08Moore\u2019s Law\uff09<\/span><\/span>\u63cf\u8ff0\u4e86\u786c\u4ef6\u5bc6\u5ea6\u7684\u63d0\u5347\u8d8b\u52bf\uff1b\u8d85\u7ea7\u8ba1\u7b97\u673a\u7684\u8ba1\u7b97\u80fd\u529b\u4ee5\u6bcf\u79d2\u6d6e\u70b9\u8fd0\u7b97\u6b21\u6570<\/span><\/span>\uff08FLOPS, Floating-Point Operations Per Second\uff09<\/span><\/span>\u6765\u8861\u91cf\u3002\u8fd9\u4e00\u7edf\u4e00\u5ea6\u91cf\u65b9\u5f0f\uff0c\u4e3a\u4ece\u8d44\u6e90\u53d7\u9650\u7684\u5fae\u63a7\u5236\u5668\u5230\u6ee1\u8db3HPC\u9700\u6c42\u7684\u670d\u52a1\u5668\u7ea7\u5904\u7406\u5668\u63d0\u4f9b\u4e86\u6027\u80fd\u8bc4\u4f30\u4f9d\u636e\u3002\u56e0\u6b64\uff0c\u4ee5\u7c7b\u4f3c\u7684\u6269\u5c55\u5ea6\u91cf\u4f53\u7cfb\u8861\u91cf\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u89c4\u6a21\u5316\u53d1\u5c55\u662f\u5408\u7406\u7684\u3002\u8fd9\u79cd\u65b9\u6cd5\u65e2\u80fd\u91cf\u5316\u8ba1\u7b97\u6027\u80fd<\/span><\/span>\uff08\u8ba1\u7b97\u64cd\u4f5c\u6570\u91cf\uff09<\/span><\/span>\uff0c\u4e5f\u80fd\u76f4\u89c2\u5730\u7c7b\u6bd4\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u7684\u795e\u7ecf\u5143\u89c4\u6a21\uff0c\u4f8b\u5982\u5c06\u5176\u4e0e\u4e0d\u540c\u52a8\u7269\u5927\u8111\u7684\u795e\u7ecf\u5143\u6570\u91cf\u8fdb\u884c\u5bf9<\/span><\/span>\u5e94[6]\u3002\u7136\u800c\uff0c\u5c3d\u7ba1\u8fd9\u4e9b\u5ea6\u91cf\u65b9\u5f0f\u80fd\u591f\u53cd\u6620\u8ba1\u7b97\u80fd\u529b\u7684\u63d0\u5347\uff0c\u4f46\u5b83\u4eec\u4f4e\u4f30\u4e86\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u72ec\u7279\u7279\u6027\u3002\u89c4\u6a21\u5316\u53d1\u5c55\u5e26\u6765\u4e86\u5236\u9020\u3001\u6d4b\u8bd5\u548c\u53ef\u9760\u6027\u7b49\u65b9\u9762\u7684\u6311\u6218\uff0c\u5c24\u5176\u662f\u5728\u975e\u53d7\u63a7\u73af\u5883\u4e0b\u7684\u6027\u80fd\u8868\u73b0\u3001\u57fa\u7840\u8bbe\u65bd\u9002\u914d\u548c\u7528\u6237\u53ef\u7528\u6027\u7b49\u65b9\u9762\u3002<\/span><\/span><\/section>\n

    <\/span><\/span><\/section>\n
    \u76ee\u524d\uff0c\u8be5\u9886\u57df\u7684\u79d1\u7814\u4e0e\u521d\u521b\u6295\u8d44\u6b63\u5728\u7a7a\u524d\u589e\u957f\uff0c\u540c\u65f6\u5668\u4ef6\u4e0e\u67b6\u6784\u4e5f\u5728\u4e0d\u65ad\u8d8b\u4e8e\u6210\u719f[2]\u3002\u795e\u7ecf\u79d1\u5b66\u6b63\u5728\u52a0\u6df1\u5bf9\u5927\u8111\u673a\u5236\u7684\u7406\u89e3[7]\uff0c\u8fdb\u800c\u542f\u53d1\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5de5\u7a0b\u5e08\uff0c\u63a8\u52a8\u611f\u77e5\u8ba1\u7b97\u7cfb\u7edf\u7684\u521b\u65b0\uff0c\u5e76\u62d3\u5c55\u81f3\u65b0\u7684\u5e94\u7528\u9886\u57df[8]\u3002\u5df2\u6709\u591a\u4e2a\u9886\u57df\u7684\u6982\u5ff5\u9a8c\u8bc1\u6848\u4f8b\u5c55\u793a\u4e86\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u6f5c\u529b\uff0c\u4f8b\u5982\u79d1\u5b66\u8ba1\u7b97\uff0c\u4eba\u5de5\u89c6\u89c9[9,10]\uff0c\u673a\u5668\u4eba\u6280\u672f[11]\uff0c\u751f\u7269\u4fe1\u53f7\u5904\u7406[12]\uff0c\u592a\u7a7a\u8ba1\u7b97\uff0c\u8ba1\u7b97\u795e\u7ecf\u79d1\u5b66[13,14]\u3002\u8fd9\u4e9b\u6848\u4f8b<\/span><\/span>\uff08\u89c1\u56fe2\uff09<\/span><\/span>\u8bc1\u660e\u4e86\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u53ef\u884c\u6027\uff0c\u4f46\u4e0e\u771f\u6b63\u7684\u89c4\u6a21\u5316\u53d1\u5c55\u4e4b\u95f4\u4ecd\u7136\u5b58\u5728\u8f83\u5927\u9e3f\u6c9f\u3002\u63a8\u52a8<\/span><\/span>\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u89c4\u6a21\u5316\u7684\u5173\u952e\u5728\u4e8e\u660e\u786e\u89c4\u6a21\u5316\u7cfb\u7edf\u6240\u9700\u7684\u6838\u5fc3\u7279\u6027\uff0c\u4ee5\u53ca\u786e\u5b9a\u6700\u6709\u5229\u4e8e\u5176\u5e7f\u6cdb\u5e94\u7528\u7684\u6761\u4ef6\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u91cd\u70b9\u63a2\u8ba8\u5bf9\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u7814\u7a76\u3001\u5de5\u7a0b\u8bbe\u8ba1\u548c\u5e94\u7528\u8d77\u5230\u5173\u952e\u4f5c\u7528\u7684\u5404\u4e2a\u65b9\u9762\u3002<\/span><\/span><\/section>\n

    <\/span><\/span><\/section>\n
    <\/span><\/span><\/section>\n

    \u56fe2\uff1a\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001\u7cfb\u7edf<\/span><\/span><\/h4>\n

    <\/span><\/section>\n

    \u672c\u6587\u6240\u63cf\u8ff0\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001<\/span><\/span>\u7cfb\u7edf\u7531\u4ee5\u4e0b\u51e0\u4e2a\u5c42\u6b21\u6784\u6210\uff1a<\/span><\/span><\/p>\n

      \n
    1. \n

      \u786c\u4ef6\u4e0e\u8f6f\u4ef6\u6280\u672f\uff1a\u786c\u4ef6\u5305\u62ec\u795e\u7ecf\u5f62\u6001\u82af\u7247\u4e0e\u8ba1\u7b97\u677f[123,124,125]\uff0c\u8f6f\u4ef6\u6db5\u76d6\u5219\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u6846\u67b6\u4e0e\u5de5\u5177\u94fe\u2076\u2076 [66,67,72,77,126,127]\u3002<\/span><\/span><\/p>\n<\/li>\n

    2. \n

      \u786c\u4ef6\/\u8f6f\u4ef6\u534f\u540c\u8bbe\u8ba1<\/span><\/span>\uff08Co-Design\uff09<\/span><\/span>\uff1a\u901a\u8fc7\u8f6f\u786c\u4ef6\u8054\u5408\u4f18\u5316\uff0c\u63d0\u9ad8\u7cfb\u7edf\u7684\u8ba1\u7b97\u6548\u7387\u548c\u7075\u6d3b\u6027\u3002<\/span><\/span><\/p>\n<\/li>\n

    3. \n

      \u5de5\u4f5c\u6d41<\/span><\/span>\uff08Workflow\uff09<\/span><\/span>\uff1a\u5f62\u6210\u5b8c\u6574\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001[4,128,129]\uff0c\u652f\u6301\u4e0d\u540c\u4efb\u52a1\u7684\u9002\u914d\u3002<\/span><\/span><\/p>\n<\/li>\n

    4. \n

      \u539f\u578b\u7cfb\u7edf\u53ca\u5e94\u7528\u9886\u57df\uff1a\u57fa\u4e8e\u5d4c\u5165\u5f0f\u4f20\u611f\u4e0e\u6267\u884c\u7cfb\u7edf<\/span><\/span>\uff08Sensors[55] & Actuators[130,132]\uff09<\/span><\/span>\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u677f<\/span><\/span>\uff08Prototype Boards\uff09<\/span><\/span>\u5728\u5177\u4f53\u9886\u57df[9,10,14,128,130,131]\u7684\u5e94\u7528\u3002<\/span><\/span><\/p>\n<\/li>\n

    5. \n

      \u5916\u90e8\u5de5\u5177\u53ca\u6846\u67b6\u96c6\u6210\uff1a\u901a\u8fc7\u4e00\u7cfb\u5217\u5916\u90e8\u5de5\u5177[133,134,135,136,137]\uff0c\u4e0e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u6846\u67b6\u8fdb\u884c\u96c6\u6210\u3002<\/span><\/span><\/p>\n<\/li>\n

    6. \n
      \u793e\u533a\u53cd\u9988\u4e0e\u7814\u53d1\u63a8\u52a8\uff1a\u7814\u7a76\u793e\u533a\u7684\u53cd\u9988\u673a\u5236\u8d77\u5230\u7814\u53d1\u52a0\u901f\u5668\u7684\u4f5c\u7528\u3002<\/span><\/section>\n<\/li>\n<\/ol>\n

      <\/span><\/span><\/section>\n
      \u5728\u6570\u636e\u4e2d\u5fc3\u4e2d\u5229\u7528\u591a\u4e2a\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u8fdb\u884c\u8fdc\u7a0b\u8bbf\u95ee\uff0c\u901a\u8fc7\u8fb9\u7f18\u8bbe\u5907\u6784\u6210\u7684\u5927\u89c4\u6a21\u667a\u80fd\u7f51\u7edc\uff0c\u5b9e\u73b0\u5206\u5e03\u5f0f\u8ba1\u7b97\uff0c\u6216\u8005\u7ed3\u5408\u6570\u636e\u4e2d\u5fc3\u8ba1\u7b97\u4e0e\u8fb9\u7f18\u667a\u80fd\uff0c\u4ece\u800c\u5f62\u6210\u5b8c\u6574\u751f\u6001\uff0c\u4e3a\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u5e7f\u6cdb\u5e94\u7528\u63d0\u4f9b\u4e86\u57fa\u7840\u67b6\u6784\u652f\u6301\u3002<\/span><\/span><\/section>\n

      <\/span><\/span><\/section>\n

      <\/span><\/h3>\n

      \n
      \n
      \n
      \n
      \n

      <\/span><\/section>\n

      <\/span><\/section>\n
      \n
      <\/section>\n<\/section>\n<\/section>\n
      \n

      \u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u5173\u952e\u7279\u6027<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

      \n

      <\/span><\/section>\n

      <\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n


      <\/span><\/p>\n

      \u5982\u4f55\u786e\u5b9a\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u76f8\u6bd4\u4e8e\u6df1\u5ea6\u5b66\u4e60\u52a0\u901f\u5668\u6216\u4f20\u7edf\u51af\u00b7\u8bfa\u4f9d\u66fc<\/span><\/span>\uff08Von Neumann\uff09<\/span><\/span>\u5904\u7406\u5668\uff0c\u5177\u6709\u66f4\u9ad8\u6548\u7387\u548c\u66f4\u5f3a\u6269\u5c55\u6027\u7684\u7279\u6027\uff0c\u8fd9<\/span><\/span>\u4ecd\u7136\u662f\u4e00\u4e2a\u6311\u6218\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u8bc6\u522b\u51fa\u4e00\u7cfb\u5217\u5bf9\u4e8e\u5b9e\u73b0\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u4f18\u52bf\u7684\u5173\u952e\u7279\u6027<\/span><\/span>\uff08\u89c1\u56fe3\uff09<\/span><\/span>\u3002\u9700\u8981\u5f3a\u8c03\u7684\u662f\uff0c\u8fd9\u4e9b\u7279\u6027\u5e76\u4e0d\u4f1a\u53d6\u4ee3\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u6838\u5fc3\u7279\u6027[2]\uff0c\u800c\u662f\u4e3a\u4e86\u5b9e\u73b0\u89c4\u6a21\u5316\u90e8\u7f72\u9700\u8981\u8fdb\u4e00\u6b65\u4f18\u5316\u3002<\/span><\/span><\/section>\n

      <\/span><\/span><\/section>\n
        \n
      1. \n

        \u5206\u5e03\u5f0f\u4e0e\u5c42\u6b21\u5316\uff08Distributed and Hierarchical\uff09<\/span><\/span><\/strong><\/h3>\n<\/li>\n<\/ol>\n

        <\/span><\/span><\/section>\n
        \u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u5e94\u5f53\u5177\u5907\u5206\u5e03\u5f0f\u8ba1\u7b97\u80fd\u529b\uff0c\u5e76\u91c7\u7528\u5c42\u6b21\u5316\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u5927\u8111\u89c6\u89c9\u76ae\u5c42<\/span><\/span>\uff08Visual Cortex\uff09<\/span><\/span>\u7b49\u533a\u57df\u7684\u5c42\u7ea7\u7ec4\u7ec7\u65b9\u5f0f[15,16]\u3002\u8fd9\u79cd\u67b6\u6784\u80fd\u591f\u89e3\u8026\u590d\u6742\u8f93\u5165[17,18]\uff0c\u5728\u4e0d\u540c\u5c42\u6b21\u5904\u7406\u4e0d\u540c\u5c3a\u5ea6\u7684\u4fe1\u606f[18]\uff0c\u589e\u5f3a\u5927\u89c4\u6a21\u6a21\u578b\u7684\u63a7\u5236\u80fd\u529b\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u53ef\u89e3\u91ca\u6027\uff0c\u901a\u8fc7\u4fe1\u606f\u89e3\u8026<\/span><\/span>\uff08Disentangling\uff09<\/span><\/span>\u7b80\u5316\u6570\u636e\u5e76\u51cf\u5c11\u8ba1\u7b97\u5197\u4f59\uff0c\u907f\u514d\u7ebf\u6027\u6216\u975e\u5c42\u6b21\u5316\u7f51\u7edc[19]\u7684\u4f4e\u6548\u8ba1\u7b97\uff0c\u4e0d\u9700\u8981\u7279\u6b8a\u673a\u5236[20]\u6216<\/span><\/span>\u76d1\u7763\u3002\u56e0\u6b64\uff0c\u652f\u6301\u5206\u5e03\u5f0f\u4e0e\u5c42\u6b21\u5316\u8ba1\u7b97\u662f\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5b9e\u73b0\u89c4\u6a21\u5316\u7684\u5173\u952e\u56e0\u7d20\u4e4b\u4e00\u3002<\/span><\/span><\/section>\n

        <\/span><\/span><\/section>\n
          \n
        1. \n

          \u7a00\u758f\u6027\uff08Sparsity\uff09<\/span><\/span><\/strong><\/h3>\n<\/li>\n<\/ol>\n

          <\/span><\/span><\/section>\n
          \u4eba\u8111\u5728\u6d3b\u52a8\u4e0e\u8fde\u63a5\u6a21\u5f0f\u4e0a\u8868\u73b0\u51fa\u9ad8\u5ea6\u7684\u7a00\u758f\u6027\uff0c\u8fd9\u662f\u5176\u53ef\u6269\u5c55\u6027\u7684\u5173\u952e\u56e0\u7d20\u3002\u7814\u7a76\u8868\u660e\uff0c\u5927\u8111\u7684\u7a81\u89e6\u5728\u53d1\u80b2\u8fc7\u7a0b\u4e2d\u7ecf\u5386\u7a00\u758f\u8fde\u63a5\u3001\u5bc6\u5ea6\u589e\u5f3a\u3001\u5927\u89c4\u6a21\u526a\u679d\u548c\u7a33\u5b9a\u7a00\u758f\u6027\u7ef4\u6301\u7684\u8fc7<\/span><\/span>\u7a0b[21]\u3002\u5176\u4e2d\u7a00\u758f\u6027\u53ef\u4ee5\u964d\u4f4e\u8868\u793a\u590d\u6742\u5ea6\uff0c\u4ec5\u4f7f\u7528\u5fc5\u8981\u7684\u795e\u7ecf\u5143\u548c\u8fde\u63a5\uff0c\u63d0\u5347\u8ba1\u7b97\u3001\u5b58\u50a8\u548c\u80fd\u8017\u6548\u7387\uff0c\u800c\u4e0d\u635f\u5931\u7cbe\u5ea6\u3002\u7a00\u758f\u5316\u53ef\u4ee5\u5206\u4e3a\u7ed3\u6784\u6027\u7a00\u758f<\/span><\/span>\uff08Structural Sparsity\uff09<\/span><\/span>\uff0c\u5982\u795e\u7ecf\u5143\u3001\u7a81\u89e6\u6743\u91cd\u3001\u6ce8\u610f\u529b\u5934\u7684\u526a\u679d\uff0c\u4ee5\u53ca\u77ac\u65f6\u7a00\u758f<\/span><\/span>\uff08Ephemeral Sparsity\uff09<\/span><\/span>\uff0c\u5982\u6fc0\u6d3b\u503c\u3001\u68af\u5ea6\u3001\u8bef\u5dee\u7684\u7a00\u758f\u5316[22]\u3002\u5c3d\u7ba1\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5929\u7136\u652f\u6301\u4e8b\u4ef6\u9a71\u52a8<\/span><\/span>\uff08Event-Driven\uff09<\/span><\/span>\u8ba1\u7b97[23,24,25,26]\uff0c\u4f46\u4ecd\u9700\u8fdb\u4e00\u6b65\u63a2\u7d22\u7a00\u758f\u673a\u5236\u4ee5\u5b9e\u73b0\u66f4\u5927\u89c4\u6a21\u8ba1\u7b97\u548c\u6cdb\u5316\u80fd\u529b\u3002\u6b64\u5916\uff0c\u7a00\u758f\u6027\u8fd8\u53ef\u80fd\u901a\u8fc7\u91c7\u7528\u975e\u4f20\u7edf\u7684\u7a7a\u95f4\u5e03\u5c40[27]\u6765\u52a0\u5feb\u6a21\u578b\u6269<\/span><\/span>\u5c55\u901f\u5ea6\u3002<\/span><\/span><\/section>\n

          <\/span><\/span><\/section>\n
            \n
          1. \n

            \u795e\u7ecf\u5143\u53ef\u6269\u5c55\u6027\uff08Neuronal Scalability\uff09<\/span><\/span><\/strong><\/h3>\n<\/li>\n<\/ol>\n

            <\/span><\/span><\/section>\n
            \u795e\u7ecf\u5143\u53ef\u62d3\u5c55\u6027\u6307\u80fd\u591f\u652f\u6301\u5927\u89c4\u6a21\u795e\u7ecf\u5143\u7684\u5355\u82af\u7247\u6216\u591a\u82af\u7247\u7cfb\u7edf\uff0c\u4f7f\u6df1\u5ea6\u8109\u51b2\u795e\u7ecf\u7f51\u7edc<\/span><\/span>\uff08Spiking Neural Network, SNN\uff09<\/span><\/span>\u6216\u57fa\u4e8e\u8109\u51b2\u9891\u7387\u7684\u7b97\u6cd5\u80fd\u591f\u5728\u5404\u7c7b\u673a\u5668\u5b66\u4e60\u5e94\u7528\u4e2d\u89e3\u51b3\u590d\u6742\u73b0\u5b9e<\/span><\/span>\u95ee\u9898[23,28,29]\u3002\u8fd9\u79cd\u53ef\u6269\u5c55\u6027\u8fd8\u4f7f\u6211\u4eec\u80fd\u591f\u5b9e\u65f6\u6a21\u62df\u5b8c\u6574\u7684\u4eba\u8111\uff0c\u4ece\u800c\u63a8\u52a8\u8ba4\u77e5\u8ba1\u7b97\u548c\u795e\u7ecf\u79d1\u5b66\u7814\u7a76\u7684\u53d1\u5c55\u3002\u76ee\u524d\u89c4\u6a21\u5316\u8ba1\u7b97\u53ef\u4ee5\u901a\u8fc7\u5806\u53e0\u591a\u4e2a\u795e\u7ecf\u5f62\u6001\u82af\u7247\u6765\u5b9e\u73b0<\/span><\/span>\uff08\u89c1\u56fe1\uff09<\/span><\/span>\uff0c\u652f\u6301\u6570\u4ebf\u4e2a\u795e\u7ecf\u5143\u7684\u8ba1\u7b97\u80fd\u529b\uff0c\u6781\u5927\u5730\u62d3\u5c55\u4e86\u5e94\u7528\u8303\u56f4\uff0c\u4f8b\u5982\u6c42\u89e3\u975e\u786e\u5b9a\u6027\u591a\u9879\u5f0f\u65f6\u95f4<\/span><\/span>\uff08NP\uff09<\/span><\/span>\u5b8c\u5168\u95ee\u9898\u3001\u8fd0\u884c\u5927\u89c4\u6a21\u795e\u7ecf\u6a21\u62df\u6216\u6267\u884c\u590d\u6742\u56fe\u7b97\u6cd5[30,31]\u3002<\/span><\/span><\/section>\n

            <\/span><\/span><\/section>\n
            \u8981\u63a8\u52a8\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u66f4\u5e7f\u6cdb\u5e94\u7528\uff0c\u5fc5\u987b\u5f15\u5165\u53ef\u96c6\u6210\u4e0d\u540c\u8ba1\u7b97\u8d44\u6e90\u548c\u795e\u7ecf\u5f62\u6001\u5143\u4ef6[32,33]\u7684\u7279\u6027\uff0c\u5e76\u652f\u6301\u5916\u90e8\u5de5\u5177\u548c\u4f20\u611f\u5668\u7684\u878d\u5408\uff0c\u8fd9\u79cd\u80fd\u529b\u88ab\u79f0\u4e3a\u5f02\u6784\u96c6\u6210<\/span><\/span>\uff08Heterogeneous Integration\uff09<\/span><\/span>[34,35]\uff0c\u5373\u80fd\u591f\u5728\u5355\u4e2a\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u5185\u6574\u5408\u591a\u79cd\u8bbe\u5907\u6280\u672f\uff0c\u8fdb\u884c\u591a\u4f20\u611f\u5668\u6570\u636e\u878d\u5408\uff0c\u5e76\u652f\u6301\u5927\u89c4\u6a21\u6df7\u5408\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc<\/span><\/span>\uff08ANN\uff09<\/span><\/span>\u548c\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u6846\u67b6\u7684\u90e8\u7f72\u3002<\/span><\/span><\/section>\n

            <\/span><\/span><\/section>\n
              \n
            1. \n

              \u5f02\u6b65\u901a\u4fe1\uff08Asynchronous communication\uff09<\/span><\/span><\/strong><\/h3>\n<\/li>\n<\/ol>\n

              <\/span><\/span><\/section>\n
              \u8bb8\u591a\u795e\u7ecf\u5f62\u6001\u82af\u7247\u91c7\u7528\u57fa\u4e8e\u4e8b\u4ef6\u7684\u5f02\u6b65\u901a\u4fe1\u534f\u8bae\u6765\u652f\u6301\u5730\u5740\u4e8b\u4ef6\u8868\u793a<\/span><\/span>\uff08AER, Address Event Representation\uff09<\/span><\/span>\u67b6\u6784\u4e0b\u7684\u6570\u636e\u6536\u53d1\u3002\u5730\u5740\u4e8b\u4ef6<\/span><\/span>\uff08Address Events\uff09<\/span><\/span>\u4f5c\u4e3a\u82af\u7247\u7684\u8f93\u5165\uff0c\u4ee3\u8868\u63a5\u6536\u8f93\u5165\u4e8b\u4ef6\u6216\u8109\u51b2\u7684\u795e\u7ecf\u5143[36]\u3002\u8fd9\u4e9b<\/span><\/span>\u534f\u8bae\u5df2\u6709\u591a\u4e2a\u53d8\u4f53\uff0c\u5e76\u80fd<\/span><\/span>\u591f\u5728\u4e0d\u540c\u82af\u7247\u67b6\u6784\u4e2d\u5b9e\u73b0\uff0c\u5982\u7b2c\u4e00\u4ee3\u82af\u7247<\/span><\/span>\uff08\u5982\u7845\u89c6\u7f51\u819c\u611f\u77e5\u7cfb\u7edf\uff09<\/span><\/span>\u3001\u76ae\u5c42\u8ba4\u77e5\u5904\u7406\u7cfb\u7edf[3,37,38]\u3001\u590d\u6742\u7247\u4e0a\u7f51\u7edc<\/span><\/span>\uff08NoC, Network on Chip\uff09<\/span><\/span>\u67b6\u6784[32,39,40,41]\u7b49\u3002\u8fd9\u4e9b\u8fdb\u5c55\u4f7f\u5f97\u5f02\u6b65\u901a\u4fe1\u6280\u672f\u66f4\u52a0\u6210\u719f\uff0c\u5e76\u4fc3\u8fdb\u4e86\u5927\u89c4\u6a21\u7cfb\u7edf\u7684\u96c6\u6210\u3002<\/span><\/span><\/section>\n

              <\/span><\/span><\/section>\n
                \n
              1. \n

                \u52a8\u6001\u53ef\u91cd\u6784\u6027\uff08Dynamic reconfigurability\uff09<\/span><\/span><\/strong><\/h3>\n<\/li>\n<\/ol>\n

                <\/span><\/span><\/section>\n
                \u5927\u8111\u672c\u8d28\u4e0a\u662f\u4e00\u4e2a\u52a8\u6001\u7cfb\u7edf\u3002\u7814\u7a76\u8868\u660e\uff0c\u6267\u884c\u8ba4\u77e5\u4efb\u52a1\u9700\u8981\u5927\u8111\u533a\u57df\u4e4b\u95f4\u7684\u52a8\u6001\u7f51\u7edc\u91cd\u7ec4\uff0c\u5e76\u5f62\u6210\u590d\u6742\u4e14\u77ac\u65f6\u7684\u901a\u4fe1\u6a21\u5f0f[42]\u3002\u4e3a\u4e86\u5b9e\u73b0\u7c7b\u4f3c\u529f\u80fd\uff0c\u591a\u4e2a\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u9700\u8981\u652f\u6301\u4e0d\u540c\u5f62\u5f0f\u7684\u52a8\u6001\u53ef\u91cd\u6784\u6027[23,43,44,45,46]\u3002\u8fd9\u79cd\u80fd\u529b\u53ef\u4ee5\u4f5c\u7528\u4e8e\u57fa\u7840\u8ba1\u7b97\u5355\u5143<\/span><\/span>\uff08\u5982\u7a81\u89e6\u3001\u795e\u7ecf\u5143\u3001\u8f74\u7a81\u3001\u6811\u7a81\uff09<\/span><\/span>\u6216\u6574\u4f53\u7cfb\u7edf\u3002\u5728\u67d0<\/span><\/span>\u4e9b\u82af\u7247\u4e2d\uff0c\u7a81\u89e6\u8fde\u63a5\u7684\u53ef\u91cd\u6784\u6027\u548c\u4e00\u5bf9\u591a\u901a\u4fe1\u80fd\u529b\u53ef\u4ee5\u7528\u4e8e\u7ebf\u6027\u6ee4\u6ce2\uff0c\u5305\u62ec\u8fb9\u7f18\u68c0\u6d4b\u548c\u5e73\u6ed1\u8fd0\u7b97\u3002<\/span><\/span><\/section>\n

                <\/span><\/span><\/section>\n
                  \n
                1. \n

                  \u5197\u4f59\u6027\u4e0e<\/span><\/span>\u76f8\u5173\u6027\uff08Redundancy and correlation\uff09<\/span><\/span><\/strong><\/h3>\n<\/li>\n<\/ol>\n

                  <\/span><\/span><\/section>\n
                  \u795e\u7ecf\u5143\u6267\u884c\u591a\u79cd\u4efb\u52a1\u7684\u80fd\u529b\u90e8\u5206\u5f52\u56e0\u4e8e\u795e\u7ecf\u5197\u4f59\u6027\u3002\u795e\u7ecf\u5143\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u5f71\u54cd\u7740\u6570\u636e\u5728\u795e\u7ecf\u5143\u7fa4\u4f53\u4e2d\u7684\u7f16\u7801\u65b9\u5f0f\u548c\u7fa4\u4f53\u795e<\/span><\/span>\u7ecf\u8ba1\u7b97\u7684\u89e3\u7801\u65b9\u6cd5[47]\u3002\u7814\u7a76\u8868\u660e\uff0c\u5197\u4f59\u7684\u6d3b\u52a8\u6a21\u5f0f\u53ef\u80fd\u5bf9\u795e\u7ecf\u8ba1\u7b97\u6709\u5229[48]\uff0c\u4f7f\u8ba1\u7b97\u5728\u795e\u7ecf\u52a8\u6001\u4e0d\u7a33\u5b9a\u7684\u60c5\u51b5\u4e0b\u4ecd\u80fd\u4fdd\u6301\u7a33\u5b9a\u5e76\u6709\u6548\u6ee4\u9664\u566a\u58f0\u3002\u5c3d\u7ba1\u5df2\u6709\u591a\u4e2a\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf[3,32,39,49]\u652f\u6301\u7c7b\u4f3c\u673a\u5236<\/span><\/span>\uff08\u5982\u8df3\u96f6\uff08Skip-Zero\uff09\u4e0e\u96f6\u6743\u91cd\uff08Zero-Weight\uff09\u65b9\u6cd5\uff09<\/span><\/span>\uff0c\u4f46\u4ecd\u7136\u6709\u6269\u5c55\u7a7a\u95f4\u3002<\/span><\/span><\/section>\n

                  <\/span><\/span><\/section>\n
                    \n
                  1. \n

                    \u4f20\u611f\u5668\u4e0e\u8ba1\u7b97\u7aef\u7684\u63a5\u53e3\uff08Sensor and compute interfaces\uff09<\/span><\/span><\/strong><\/h3>\n<\/li>\n<\/ol>\n

                    <\/span><\/span><\/section>\n
                    \u9664\u6838\u5fc3\u8ba1\u7b97\u7cfb\u7edf\u5916\uff0c\u5e94\u7528\u7684\u7aef\u5230\u7aef\u5f00\u53d1\u8fd8\u9700\u8981\u5c06\u5916\u90e8\u4f20\u611f\u5668\u548c\u6267\u884c\u5668\u9ad8\u6548\u96c6\u6210\u81f3\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5de5\u5177\u94fe\u3002\u76ee\u524d\uff0c\u5927\u591a\u6570\u5546\u7528\u4f20\u611f\u5668\u91c7\u7528\u975e\u8109\u51b2\u5f0f\u534f\u8bae\uff0c\u9700\u8981\u5185\u6838\u7ea7\u63a5\u53e3\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u8109\u51b2\u7f16\u7801\u683c\u5f0f\u3002\u4f18\u5316\u7684\u795e\u7ecf\u5f62\u6001\u9a71\u52a8\u53ef\u4ee5\u6781\u5927\u63d0\u5347\u4f4e\u5ef6\u8fdf\u4efb\u52a1<\/span><\/span>\uff08\u5982\u97f3\u9891\u548c\u89c6\u89c9\u5904\u7406\uff09<\/span><\/span>\u7684\u6027\u80fd[50,51]\u3002\u901a\u7528\u795e\u7ecf\u4fe1\u606f\u683c\u5f0f<\/span><\/span>\uff08Neural Information Format\uff09<\/span><\/span>\u7684\u91c7\u7528[52,53,54]\u80fd\u591f\u4f7f\u795e\u7ecf\u5f62\u6001\u9a71\u52a8\u66f4\u52a0\u901a\u7528\u5316\uff0c\u907f\u514d\u786c\u4ef6\u4f9d\u8d56\u3002\u795e\u7ecf\u5f62\u6001\u4f20\u611f\u5668<\/span><\/span>\uff08\u5982\u4e8b\u4ef6\u9a71\u52a8\u76f8\u673a[55]\uff09<\/span><\/span>\u53ef\u8fdb\u4e00\u6b65\u51cf\u5c11\u54cd\u5e94\u65f6\u95f4\uff0c\u9002\u7528\u4e8e\u9ad8\u901f\u8fd0\u52a8\u6355\u6349\u4efb\u52a1\u3002\u533b\u7597\u8bbe\u5907<\/span><\/span>\uff08\u5982\u7845\u7535\u5b50\u8033\u8717[56]\u3001\u7535\u5b50\u76ae\u80a4\u5047\u80a2[57]\uff09<\/span><\/span>\u5219\u53ef<\/span><\/span>\u63d0\u5347\u611f\u77e5\u7684\u7cbe\u5ea6\u4e0e\u7075\u654f\u5ea6\u3002<\/span><\/span><\/section>\n

                    <\/span><\/span><\/section>\n
                      \n
                    1. \n

                      \u8d44\u6e90\u611f\u77e5\uff08Resource awareness\uff09<\/span><\/span><\/strong><\/h3>\n<\/li>\n<\/ol>\n

                      <\/span><\/span><\/section>\n
                      \u4e3a\u4e86\u63d0\u5347\u6548\u7387\u4e0e\u591a\u529f\u80fd\u6027\uff0c\u52a0\u901f\u5668\u7684\u8bbe\u8ba1\u5fc5\u987b\u5177\u5907\u8d44\u6e90\u611f\u77e5\u80fd\u529b\u3002\u8d44\u6e90\u611f\u77e5\u6307\u7cfb\u7edf\u5728\u8fd0\u884c\u8fc7\u7a0b\u4e2d\u8ffd\u8e2a\u81ea\u8eab\u7684\u80fd\u8017\u3001\u8ba1\u7b97\u4e0e\u5b58\u50a8\u8d44\u6e90\uff0c\u5e76\u6839\u636e\u4efb\u52a1\u9700\u6c42\u52a8\u6001\u8c03\u6574\u8d44\u6e90\u5206\u914d\u7684\u80fd\u529b\u3002\u81ea\u9002\u5e94<\/span><\/span>\uff08Self-Aware\uff09<\/span><\/span>\u67b6\u6784\u548c\u6301\u7eed\u5b66\u4e60<\/span><\/span>\uff08Continual Learning\uff09<\/span><\/span>[<\/span><\/span>58<\/span><\/span>]\u52a0\u901f\u5668\u63d0\u4f9b\u4e86\u73b0\u8fd9\u4e00\u7279\u6027\u7684\u53ef\u501f\u9274\u601d\u8def\u3002<\/span><\/span><\/section>\n

                      <\/span><\/span><\/section>\n
                      <\/span><\/span><\/section>\n

                      \u56fe3\uff1a\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u5173\u952e\u7279\u6027\u53ca\u5176\u6210\u719f\u5ea6\u6f14\u8fdb\u65f6\u95f4\u7ebf<\/span><\/span><\/h4>\n

                      <\/span><\/section>\n
                      \u8fd9\u4e9b\u7279\u6027\u589e\u5f3a\u4e86\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u6838\u5fc3\u7279\u5f81\uff0c\u5e76\u5728\u591a\u4e2a\u65b9\u9762\u76f4\u63a5\u501f\u9274\u4e86\u5927\u8111\u53ca\u795e\u7ecf\u5904\u7406\u673a<\/span><\/span>\u5236[138,139]\u3002\u4e0d\u540c\u7279\u6027\u7684\u6210\u719f\u5ea6\u53d1\u5c55\u5177\u6709\u4e0d\u540c\u7684\u65f6\u95f4\u5c3a\u5ea6\uff1a\u90e8\u5206\u7279\u6027\u5c06\u5728\u77ed\u671f\u5185\u8fbe\u5230\u8f83\u9ad8\u7684\u6210\u719f\u6c34\u5e73\uff0c\u53e6\u4e00\u4e9b\u7279\u6027\u53ef\u80fd\u9700\u8981\u66f4\u590d\u6742\u7684\u6280\u672f\u624b\u6bb5\u624d\u80fd\u5b9e\u73b0\u76f8\u540c\u6c34\u5e73\u7684\u6210\u719f\u5ea6\u3002<\/span><\/span><\/section>\n

                      <\/span><\/span><\/section>\n
                      \u6bcf\u79cd\u5173\u952e\u7279\u6027\u90fd\u53ef\u4ee5\u4ece\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\u8fdb\u4e00\u6b65\u63a2\u8ba8\u5176\u5f71\u54cd\uff0c\u4f8b\u5982\u529f\u8017<\/span><\/span>\uff08Power\uff09<\/span><\/span>\uff0c\u6027\u80fd<\/span><\/span>\uff08Performance\uff09<\/span><\/span>\uff0c\u53ef\u6269\u5c55\u6027<\/span><\/span>\uff08Scalability\uff09<\/span><\/span>\uff0c\u81ea\u9002\u5e94\u80fd\u529b<\/span><\/span>\uff08Adaptability\uff09<\/span><\/span>\u548c\u96c6\u6210\u7684\u591a\u6837\u6027<\/span><\/span>\uff08Versatility of Integration\uff09<\/span><\/span>\u3002\u6b64\u5916\u6211\u4eec\u8fd8\u6ce8\u610f\u5230\uff0c\u4e00\u4e9b\u672c\u8d28\u4e0a\u5c5e\u4e8e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u7279\u6027\u540c\u6837\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u4f20\u7edf\u8ba1\u7b97\u7cfb\u7edf\u7684\u8bbe\u8ba1\u3002\u4f8b\u5982\u6781\u7aef\u5e76\u884c\u8ba1\u7b97<\/span><\/span>\uff08Extreme Parallelism\uff09<\/span><\/span>\u662f\u5927\u89c4\u6a21\u8d85\u7ea7\u8ba1\u7b97\u673a\u67b6\u6784\u7684\u57fa\u7840\uff0c\u5e76\u4e0d\u5c40\u9650\u4e8e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u3002\u800c\u5728\u6a21\u578b\u6269\u5c55\u6027\u65b9\u9762\uff0c\u8c03\u6574\u6269\u5c55\u66f2\u7ebf\u7684\u659c\u7387\u53ef\u4ee5\u8bad\u7ec3\u6bd4\u5f53\u524d\u66f4\u5927\u89c4\u6a21\u7684\u6a21\u578b\uff0c\u4e3a\u7a81\u7834\u73b0\u6709\u8ba1\u7b97\u80fd\u529b\u9650\u5236\u5e26\u6765\u4e00\u79cd\u53ef\u80fd\u6027\uff0c\u6216\u8005\u5728\u76f8\u540c\u7b97\u529b\u4e0b\u4f7f\u7528\u8f83\u5c0f\u6a21\u578b\u4f46\u8fdb\u884c\u66f4\u957f\u65f6\u95f4\u7684\u8bad\u7ec3\u3001\u4f7f\u7528\u66f4\u5927\u89c4\u6a21\u7684\u6570\u636e\u96c6\u3002\u8fd9\u4f7f\u5f97\u672a\u6765\u8d8b\u52bf\u53ef\u80fd\u662f\u8bad\u7ec3\u5927\u91cf\u5c0f\u578b\u6a21\u578b\uff0c\u6bcf\u4e2a\u6a21\u578b\u9488\u5bf9\u7279\u5b9a\u4efb\u52a1\u7684\u6570\u636e\u96c6\u4f18\u5316\uff0c\u4ece\u800c\u907f\u514d\u4e86\u5355\u4e00\u5927\u6a21\u578b\u5728\u5c0f\u4f17\u4efb\u52a1\u4e0a\u7684\u6027\u80fd\u4e0b\u964d\uff0c\u5c24\u5176\u662f\u5728\u957f\u5c3e\u4efb\u52a1\u548c\u5c0f\u6837\u672c\u5e94\u7528\u4e2d\u8868\u73b0\u66f4\u4f18\u3002\u56e0\u6b64\uff0c\u5c3d\u7ba1\u67d0\u4e9b\u8ba1\u7b97\u8bbe\u8ba1\u6982\u5ff5\u5e76\u975e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u72ec\u6709\uff0c\u5728\u7edf\u4e00\u8ba1\u7b97\u6a21\u578b\u4e2d\u7684\u534f\u540c\u4f5c\u7528\u624d\u662f\u5176\u771f\u6b63\u7684\u4ef7\u503c\u6240\u5728\u3002<\/span><\/span><\/section>\n

                      <\/span><\/span><\/section>\n


                      <\/span><\/span><\/p>\n

                      \n
                      \n
                      \n
                      \n
                      \n

                      <\/span><\/section>\n

                      <\/span><\/section>\n
                      \n
                      <\/section>\n<\/section>\n<\/section>\n
                      \n

                      \u6311\u6218\u4e0e\u673a\u9047<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

                      \n

                      <\/span><\/section>\n

                      <\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n


                      <\/span><\/p>\n

                      \u5728\u603b\u7ed3\u4e00\u4e9b\u5173\u952e\u6311\u6218\u4e0e\u673a\u9047\u65f6\uff0c\u6211\u4eec\u9700\u8981\u6ce8\u610f\u7684\u662f\u76ee\u524d\u5bf9\u5927\u8111\u7684\u7406\u89e3\u4ecd\u7136\u6709\u9650\uff0c\u800c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5219\u6709\u671b\u4e3a\u52a0\u6df1\u5bf9\u5927\u8111\u8ba4\u77e5\u63d0\u4f9b\u673a\u4f1a\u3002<\/span><\/span><\/section>\n

                      <\/span><\/span><\/section>\n
                      \u5c3d\u7ba1\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5df2\u53d6\u5f97\u4e86\u91cd\u5927\u8fdb\u5c55\uff0c\u5e76\u5c55\u73b0\u51fa\u5728\u591a\u4e2a\u793e\u4f1a\u5e94\u7528\u9886\u57df\u6784\u5efa\u53ef\u6301\u7eed\u4e14\u7a33\u5065\u6280\u672f\u7684\u98a0\u8986\u6027\u6f5c\u529b\uff0c\u4f46\u5728\u5176\u5e7f\u6cdb\u843d\u5730\u5e76\u4ea7\u751f\u5b9e\u9645\u5f71\u54cd\u4e4b\u524d\u4ecd\u9700\u514b\u670d\u8bf8\u591a\u6311\u6218\u3002\u8fd9\u4e9b\u6311\u6218\u6d89\u53ca\u795e\u7ecf<\/span><\/span>\u5f62\u6001\u786c\u4ef6\u8bbe\u8ba1\u8005\u3001\u7528\u6237\u53ca\u7b97\u6cd5\u5f00\u53d1\u8005\uff0c\u6db5\u76d6\u4ece\u8f6f\u786c\u4ef6\u534f\u540c\u8bbe\u8ba1<\/span><\/span>\uff08Co-Design\uff09\uff08\u89c1\u68462\uff09<\/span><\/span>\u5230\u6280<\/span><\/span>\u672f\u91c7\u7eb3\u4e0e\u5546\u4e1a\u5316\u7b49\u591a\u4e2a\u5c42\u9762\u3002<\/span><\/span><\/section>\n

                      <\/span><\/span><\/section>\n

                      \u4ece\u5f00\u53d1\u8005\u7684\u89d2\u5ea6\u6765\u770b\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u9762\u4e34\u591a\u4e2a\u65b9\u9762\u7684\u6311\u6218\uff1a<\/span><\/span><\/p>\n

                        \n
                      1. \n

                        \u786c\u4ef6\u6613\u7528\u6027\uff1a\u9700\u8981\u63d0\u4f9b\u66f4\u9ad8\u7ea7\u7684\u7f16\u7a0b\u62bd\u8c61<\/span>\uff08\u7c7b\u4f3c\u4e8e\u4ece\u6c47\u7f16\u8bed\u8a00\u5411\u9762\u5411\u5bf9\u8c61\u7f16\u7a0b\u7684\u8fc7\u6e21\uff09<\/span><\/span>\uff0c\u4f7f\u5f00\u53d1\u8005\u65e0\u9700\u6df1\u5165\u7406\u89e3\u5e95\u5c42\u786c\u4ef6\u5373\u53ef\u4f7f\u7528\u3002\u7531\u4e8e\u795e\u7ecf\u5f62\u6001\u786c\u4ef6\u7684\u591a\u6837\u6027\uff0c\u6a21\u578b\u5728\u4e0d\u540c\u5e73\u53f0\u4e0a\u7684\u590d\u7528\u6027\u8f83\u5dee\uff0c\u4ece\u800c\u4f7f\u5f97\u7b97\u6cd5\u4e0e\u7279\u5b9a\u786c\u4ef6\u7d27\u5bc6\u7ed1\u5b9a\u3002\u8ba1\u7b97\u539f\u8bed<\/span>\uff08Computational Primitives\uff09<\/span><\/span>\u548c\u786c\u4ef6\u7ea6\u675f\u5728\u4e0d\u540c\u5e73\u53f0\u95f4\u5dee\u5f02\u663e\u8457\uff0c\u56e0\u6b64\u9700\u8981\u5236\u5b9a\u901a\u7528\u7684\u786c\u4ef6\u4e0e\u8f6f\u4ef6\u6807\u51c6\u4ee5\u51cf\u5c11\u8de8\u5e73\u53f0\u7b97\u6cd5\u7684\u9002\u914d\u6210\u672c\u3002<\/span><\/span><\/p>\n<\/li>\n

                      2. \n

                        \u8de8\u5e73\u53f0\u517c\u5bb9\u6027\uff1a\u786c\u4ef6\u62bd\u8c61\u5c42<\/span><\/span>\uff08HAL, Hardware Abstraction Layer\uff09<\/span><\/span>\u7684\u5f15\u5165\u5bf9\u4e8e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5e73\u53f0\u81f3\u5173\u91cd\u8981\uff0c\u8fd9\u6709\u52a9\u4e8e\u6784\u5efa\u901a\u7528\u7f16\u8bd1\u65b9\u6848<\/span><\/span>\uff08Compilation Scheme\uff09<\/span><\/span>\uff0c\u4f7f\u4efb\u610f\u8109\u51b2\u795e\u7ecf\u7f51\u7edc<\/span><\/span>\uff08SNN, Spiking Neural Network\uff09<\/span><\/span>\u6a21\u578b\u80fd\u591f\u79fb\u690d\u5230\u4e0d\u540c\u7684\u786c\u4ef6\u67b6\u6784\u4e0a\u3002\u76ee\u524d\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001\u7cfb\u7edf\u4ecd\u7136\u7f3a\u5c11\u4e00\u4e9b\u5173\u952e\u7ec4\u4ef6<\/span><\/span>\uff08\u8be6\u89c1\u56fe<\/span>4<\/span><\/span>\uff09<\/span><\/span>\u3002\u5728\u73b0\u6709\u7684\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u6846\u67b6\u4e2d\uff0c\u4e92\u64cd\u4f5c\u6027\u4ecd\u7136\u6709\u9650\uff0c\u4e0e\u4f20\u7edf\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u6846\u67b6\u7684\u9ad8\u4e92\u8054\u6027\u5b58\u5728\u663e\u8457\u5dee\u8ddd\u3002<\/span><\/span><\/span><\/p>\n<\/li>\n

                      3. \n
                        \u5f00\u53d1\u4f53\u9a8c\u4f18\u5316\uff1a\u7f3a\u4e4f\u63a8\u7406\u4f18\u5316\u5668\u3001\u62d6\u62fd\u5f0f\u7f16\u8f91\u5668\u548c\u8de8\u5e73\u53f0\u517c\u5bb9\u6027\u5de5\u5177\uff0c\u5f71\u54cd\u5f00\u53d1\u7684\u4fbf\u6377\u6027\uff0c\u5e76\u589e\u52a0\u4e86\u65b0\u7528\u6237\u7684\u5b66\u4e60\u6210\u672c\u3002\u5bf9\u4e8e\u8d44\u6df1\u5f00\u53d1\u8005\u548c\u5927\u578b\u4f01\u4e1a\u800c\u8a00\uff0c\u7f3a\u4e4f\u96c6\u6210\u7684\u4e91\u7aef\u6269\u5c55\u3001\u90e8\u7f72\u548c\u751f\u547d\u5468\u671f\u7ba1\u7406\u8d44\u6e90\u4e0d\u5229\u4e8e\u5927\u89c4\u6a21\u5f00\u53d1\u548c\u5e94\u7528\u3002\u7efc\u4e0a\uff0c\u9886\u57df\u8feb\u5207\u9700\u8981\u8de8\u6846\u67b6\u4e0e\u8de8\u786c\u4ef6\u7684\u4ea4\u53c9\u7f16\u8bd1<\/span><\/span>\uff08Cross-Compilation\uff09<\/span><\/span>\u5de5\u5177\u4ee5\u51cf\u5c11\u91cd\u590d\u5f00\u53d1\uff0c\u5e76\u4fc3\u8fdb\u751f\u6001\u7cfb\u7edf\u4e4b\u95f4\u7684\u8fc1\u79fb\u3002\u8fd1\u5e74\u6765\u5728\u5f25\u5408\u8fd9\u4e9b\u5dee\u8ddd\u65b9\u9762\u5df2\u7ecf\u53d6\u5f97\u91cd\u5927\u8fdb\u5c55\uff0c\u90e8\u5206\u5173\u952e\u6280\u672f\u7a7a\u767d\u6b63\u9010\u6b65\u88ab\u586b\u8865\uff0c\u4f8b\u5982AI \u89c6\u89c9\u5de5\u5177\u5305\u3001\u6a21\u62df\u5668\/\u5206\u6790\u5de5\u5177\u548c\u81ea\u52a8\u8d85\u53c2\u6570\u641c\u7d22\u6846\u67b6\u5df2\u96c6\u6210\u5230\u73b0\u6709\u5de5\u5177\u94fe\u4e2d<\/span><\/span>[40,50,52,53,59,60,61,62,63,64,65,66,67,68,69,70,71,72]\uff0c\u5bf9\u52a8\u6001\u67b6\u6784\u548c\u4f4e\u7ea7\u8ba1\u7b97\u539f\u8bed\u6a21\u62df\u7684\u652f\u6301\u5df2\u53ef\u4e0e\u4e3b\u6d41 AI \u5de5\u5177\u76f8\u5ab2\u7f8e\u3002\u4f46\u867d\u7136\u4f4e\u7ea7\u795e\u7ecf\u5143\u8ba1\u7b97\u539f\u8bed\u7684\u4ea4\u6362\u683c\u5f0f\u5df2\u88ab\u5e7f\u6cdb\u91c7\u7528\uff0c\u9ad8\u5c42\u795e\u7ecf\u5143\u62d3\u6251\u7ed3\u6784\u7684\u4ea4\u6362\u683c\u5f0f\u5728\u4e0d\u540c\u6846\u67b6\u95f4\u4ecd\u7136\u7f3a\u4e4f\u7edf\u4e00\u6807\u51c6\u3002\u4e3b\u6d41AI\/ML \u5de5\u5177\u94fe\u66fe\u9762\u4e34\u7c7b\u4f3c\u7684\u6311\u6218\uff0c\u6700\u7ec8\u901a\u8fc7ONNX \u683c\u5f0f\u7684\u8054\u5408\u5f00\u53d1\u548c\u5e7f\u6cdb\u91c7\u7528\u5f97\u4ee5\u7f13\u89e3[73,74]\u3002\u56e0\u6b64\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u9886\u57df\u540c\u6837\u9700\u8981\u63a8\u52a8\u7c7b\u4f3c\u6807\u51c6\u7684\u5efa\u7acb\u6216\u91c7\u7528\uff0c\u4ee5\u89c4\u8303\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\u6a21\u578b<\/span><\/span>\u7684\u63cf\u8ff0\u4e0e\u4ea4\u6362\u3002<\/span><\/span><\/span><\/section>\n<\/li>\n
                      4. \n
                        \u63d0\u9ad8\u5de5\u5177\u4e0e\u5e73\u53f0\u7684\u53ef\u8bbf\u95ee\u6027\uff1a\u63d0\u4f9b\u5168\u9762\u7684\u6587\u6863\u652f\u6301\u548c\u5065\u5168\u7684\u793e\u533a\u53cd\u9988\u673a\u5236\uff0c\u5c06\u6709\u52a9\u4e8e\u5438\u5f15\u66f4\u591a\u65b0\u7528\u6237\uff0c\u4fc3\u8fdb\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u53d1\u5c55\u3002\u8fd9\u4e00\u8fdb\u5c55\u5c06\u63a8\u52a8\u96c6\u6210\u7cfb\u7edf\u7ec4\u4ef6\u7684\u5f00\u53d1\uff0c\u5f62\u6210\u53ef\u590d\u7528\u7684\u751f\u6001\u7cfb\u7edf<\/span>\uff08\u4f8b\u5982 ROS\u3001Linux\uff09<\/span><\/span>\uff0c\u800c\u4e0d\u662f\u4e13\u6ce8\u4e8e\u7279\u5b9a\u786c\u4ef6\u4efb\u52a1\u7684\u5b64\u7acb\u529f\u80fd\u6a21\u5757\u3002<\/span><\/span><\/section>\n<\/li>\n<\/ol>\n


                        <\/span><\/span><\/h4>\n

                        <\/span><\/span><\/h4>\n

                        \u56fe4\uff1a\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u8f6f\u4ef6\u751f\u6001\u7cfb\u7edf\u4e2d\u7684\u7f3a\u53e3\u5bf9\u6bd4\uff08\u4e0eAI\/ML\u6848\u4f8b\u7814\u7a76\u5bf9\u7167\uff09<\/span><\/span><\/h4>\n

                        <\/span><\/section>\n
                        \u4e3b\u6d41\u4eba\u5de5\u667a\u80fd\/\u673a\u5668\u5b66\u4e60\u751f\u6001\u7cfb\u7edf<\/span><\/span>\uff08\u56fe\u4e2d\u4e0a\u534a\u90e8\u5206\uff0c\u6bcf\u4e2a\u6848\u4f8b\u7814\u7a76\u5bf9\u5e94\u4e00\u5217\uff09<\/span><\/span>\u5df2\u7ecf\u5efa\u7acb\u4e86\u5b8c\u5584\u7684\u5f00\u53d1\u7ba1\u9053<\/span><\/span>\uff08Pipeline\uff09<\/span><\/span>\uff0c\u5e76\u4e14\u5728\u6846\u67b6\u3001\u7f16\u8bd1\u5668\u548c\u786c\u4ef6\u4e4b\u95f4\u5b9e\u73b0\u4e86\u5e7f\u6cdb\u7684\u4e92\u64cd\u4f5c\u6027\u3002\u800c\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001\u7cfb\u7edf<\/span><\/span>\uff08\u56fe\u4e2d\u4e0b\u534a\u90e8\u5206\uff0c\u6bcf\u4e2a\u6848\u4f8b\u7814\u7a76\u5bf9\u5e94\u4e00\u5217\uff09<\/span><\/span>\u4ecd\u7136\u5b58\u5728\u8f83\u591a\u5173\u952e\u7f3a\u5931\u7ec4\u4ef6\uff0c\u9700\u8981\u8fdb\u4e00\u6b65\u5b8c\u5584\u3002\u8bc6\u522b\u5e76\u586b\u8865\u8fd9\u4e9b\u7a7a\u767d\u5bf9\u4e8e\u6784\u5efa\u66f4\u5168\u9762\u3001\u534f\u4f5c\u6027\u66f4\u5f3a\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001\u7cfb\u7edf\u81f3\u5173\u91cd\u8981\u3002\u6211\u4eec\u63d0\u4f9b\u4e86\u4e09\u4e2a\u6848\u4f8b\u7814\u7a76\u8fdb\u884c\u5bf9\u6bd4\u5206\u6790\uff1a\u6848\u4f8bA\uff0c\u5927\u89c4\u6a21\u90e8\u7f72\u4e0e\u63a8\u7406\u4f18\u5316\uff1b\u6848\u4f8bB\uff0c\u56fe\u795e\u7ecf\u7f51\u7edc<\/span><\/span>\uff08Graph Neural Networks, GNN\uff09<\/span><\/span>\u7684\u5f00\u53d1\u4e0e\u5206\u6790\uff1b\u6848\u4f8bC\uff0cAI\u6a21\u578b\u5230\u901a\u7528\u6216\u7279\u5b9a\u786c\u4ef6\u7684\u7f16\u8bd1\u3002\u56fe\u4e2d\u5fc3\u6807\u6ce8\u4e86AI\/ML \u751f\u6001\u7cfb\u7edf\u548c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u4e2d\u5171\u6709\u7684\u8f6f\u4ef6\u7ec4\u4ef6\uff0c\u800c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7f3a\u5931\u7684\u8f6f\u4ef6\u7ec4\u4ef6\u5219\u4ee5\u7a7a\u767d\u5757\u8868\u793a\u3002\u5f53\u524d\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001\u7cfb\u7edf\u5c1a\u672a\u5b8c\u5584\u7684\u90e8\u5206\uff0c\u9700\u8981\u8fdb\u4e00\u6b65\u7684\u5de5\u5177\u548c\u6846\u67b6\u5f00\u53d1\uff0c\u4ee5\u5b9e\u73b0\u4e0e\u4eba\u5de5\u667a\u80fd\/\u673a\u5668\u5b66\u4e60\u9886\u57df\u76f8\u7c7b\u4f3c\u7684\u4e92\u64cd\u4f5c\u6027\u548c\u751f\u6001\u6210\u719f\u5ea6\u3002<\/span><\/span><\/section>\n

                        <\/span><\/span><\/section>\n
                        \u79d1\u5b66\u7814\u7a76\u793e\u533a\u504f\u597d\u5de5\u5177\u7684\u7075\u6d3b\u6027\uff0c\u800c\u7ec8\u7aef\u7528\u6237\u548c\u4ea7\u54c1\u8bbe\u8ba1\u5e08\u66f4\u6ce8\u91cd\u5de5\u5177\u7684\u6548\u7387\u3002\u56e0\u6b64\uff0c\u63d0\u9ad8\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u91c7\u7528\u7387\u7684\u4e00\u79cd\u7b56\u7565\u662f\u501f\u9274\u65e9\u671fAI\u5de5\u5177\u94fe<\/span><\/span>\uff08\u5982Torch\u548cTheano<\/span><\/span>[75,76]\uff09<\/span><\/span><\/span>\u7684\u53d1\u5c55\u6a21\u5f0f\uff0c\u5373\u5728\u65e9\u671f\u5f00\u53d1\u9636\u6bb5\u4f18\u5148\u8003\u8651\u7075\u6d3b\u6027\u800c\u975e\u6548\u7387\uff0c\u4ece\u800c\u5438\u5f15\u66f4\u5e7f\u6cdb\u7684\u79d1\u5b66\u7814\u7a76\u793e\u533a\u53c2\u4e0e\u5f00\u53d1\u3002\u8fd9\u79cd\u793e\u533a\u9a71\u52a8\u7684\u53d1\u5c55\u6a21\u5f0f\u4fc3\u4f7f\u66f4\u9ad8\u6548\u7684\u540e\u7aef<\/span><\/span>\uff08Back-End\uff09<\/span><\/span>\u8ba1\u7b97\u6846\u67b6\u7684\u8bde\u751f\uff0c\u4f8b\u5982PyTorch[77]\u3002<\/span><\/span><\/section>\n

                        <\/span><\/span><\/section>\n
                        \u4e0e\u6b64\u540c\u65f6\uff0c\u5e94\u7528\u5f00\u53d1\u8005\u9762\u4e34\u7684\u4e00\u5927\u969c\u788d\u5728\u4e8e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u9700\u8981\u4e00\u79cd\u6839\u672c\u4e0a\u4e0d\u540c\u7684\u7f16\u7a0b\u8303\u5f0f\uff0c\u5373\u7ed3\u5408\u795e\u7ecf\u8ba1\u7b97\u539f\u8bed<\/span><\/span>\uff08Primitives\uff09<\/span><\/span>\u6765\u7f16\u5199\u201c\u7a0b\u5e8f\u201d\u3002\u8fd9\u5bf9\u6ca1\u6709\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u6216\u8ba1\u7b97\u795e\u7ecf\u79d1\u5b66\u7ecf\u9a8c\u7684\u7814\u7a76\u4eba\u5458\u548c\u5de5\u7a0b\u5e08\u6784\u6210\u4e86\u8fdb\u5165\u95e8\u69db\u3002\u56e0\u6b64\uff0c\u9700\u8981\u5efa\u7acb\u575a\u5b9e\u7684\u7406\u8bba\u57fa\u7840\u6765\u6307\u5bfc\u5982\u4f55\u4f7f\u7528\u795e\u7ecf\u8ba1\u7b97\u6a21\u5757<\/span><\/span>\uff08Building Blocks\uff09<\/span><\/span>\u6765\u5b9e\u73b0\u8ba1\u7b97\u3002\u4f8b\u5982\uff0c\u5e38\u5fae\u5206\u65b9\u7a0b<\/span><\/span>\uff08ODE, Ordinary Differential Equation\uff09<\/span><\/span>\u6c42\u89e3\u5668\u53ef\u7528\u4e8e\u5efa\u6a21\u795e\u7ecf\u52a8\u529b\u5b66\u3002\u76ee\u524d\u5df2\u6709\u4e00\u4e9b\u521d\u6b65\u5de5\u4f5c\u91c7\u7528\u4e86\u6570\u5b66\u5efa\u6a21\u65b9\u6cd5\u6765\u7814\u7a76\u8fd9\u4e00\u95ee\u9898\uff0c\u4f8b\u5982\u795e\u7ecf\u5de5\u7a0b\u6846\u67b6<\/span><\/span>\uff08NEF, Neural Engineering Framework\uff09<\/span><\/span>[78]\u548c\u52a8\u6001\u795e\u7ecf\u573a<\/span><\/span>\uff08Dynamic Neural Fields\uff09<\/span><\/span>[79]\u3002<\/span><\/span><\/section>\n

                        <\/span><\/span><\/section>\n
                        \u4ece\u8bbe\u8ba1\u8005\u7684\u89d2\u5ea6\u6765\u770b\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u5f00\u53d1\u9700\u8981\u8d2f\u7a7f\u6574\u4e2a\u8ba1\u7b97\u6808<\/span><\/span>\uff08Stack\uff09<\/span><\/span>\u7684\u5404\u4e2a\u5c42\u7ea7\uff1a\u5668\u4ef6\u3001\u7535\u5b50\u7535\u8def\u3001\u8ba1\u7b97\u67b6\u6784\u3001\u7b97\u6cd5\u548c\u5e94\u7528\u7684\u53d1\u5c55\u5e94\u5728\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u4e2d\u65e0\u7f1d\u96c6\u6210\uff0c\u9ad8\u5ea6\u540c\u6b65\u5404\u4e2a\u5c42\u7ea7\u7684\u8fdb\u5c55\u4ee5\u786e\u4fdd\u7cfb\u7edf\u8bbe\u8ba1\u7684\u6574\u4f53\u6027\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u4e3a\u4e86\u4f7f\u8fd9\u4e9b\u7cfb\u7edf\u80fd\u591f\u6269\u5c55\u81f3\u590d\u6742\u4efb\u52a1\uff0c\u7cfb\u7edf\u7ec4\u4ef6\u5fc5\u987b\u5177\u6709\u6a21\u5757\u5316<\/span><\/span>\uff08Modularity\uff09<\/span><\/span>\u7279\u6027\u3002\u8fd9\u5f15\u51fa\u4e86\u4e00\u4e2a\u6838\u5fc3\u95ee\u9898\uff1a\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u5f53\u524d\u4f7f\u7528\u7684\u795e\u7ecf\u5143\u548c\u7a81\u89e6\u662f\u5426\u662f\u6b63\u786e\u7684\u62bd\u8c61\u7ea7\u522b\uff1f\u76ee\u524d\u9886\u57df\u7684\u4e3b\u6d41\u5b9e\u73b0\u57fa\u672c\u90fd\u57fa\u4e8e\u795e\u7ecf\u5143-\u7a81\u89e6\u6a21\u578b\uff0c\u4f46\u8fd9\u4e00\u62bd\u8c61\u662f\u5426\u9002\u7528\u4e8e\u672a\u6765\u7684\u89c4\u6a21\u5316\u53d1\u5c55\u4ecd\u6709\u5f85\u8ba8\u8bba\u3002\u6b64\u5916\uff0c\u795e\u7ecf\u5f62\u6001\u7b97\u6cd5\u901a\u5e38\u5f3a\u8c03\u751f\u7269\u53ef\u89e3\u91ca\u6027<\/span><\/span>\uff08Biological Plausibility\uff09<\/span><\/span>\uff0c\u4f46\u8fd9\u672a\u5fc5\u662f\u5b9e\u73b0\u6700\u4f73\u4efb\u52a1\u6027\u80fd\u3001\u53ef\u6269\u5c55\u6027\u548c\u9ad8\u80fd\u6548\u7684\u6700\u4f18\u9009\u62e9\u3002\u90e8\u5206\u539f\u56e0\u5728\u4e8e\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u57fa\u7840\u67b6\u6784\u4e0e\u751f\u7269\u795e\u7ecf\u7cfb\u7edf\u5b58\u5728\u6839\u672c\u6027\u5dee\u5f02\uff0c\u5e76\u4e14\u751f\u7269\u7cfb\u7edf\u7684\u590d\u6742\u6027\u8fdc\u8d85\u5f53\u524d\u7b80\u5316\u7684\u751f\u7269\u8ba1\u7b97\u6a21\u578b\uff0c\u8bb8\u591a\u5c42\u7ea7\u7684\u590d\u6742\u6027\u672a\u80fd\u5728\u73b0\u6709\u6a21\u578b\u4e2d\u4f53\u73b0\u3002\u6311\u6218\u5728\u4e8e\u5982\u4f55\u5f00\u53d1\u5408\u9002\u7684\u7b97\u6cd5\u62bd\u8c61\u5c42\u7ea7\uff0c\u4f7f\u6211\u4eec\u80fd\u591f\u5728\u6e05\u6670\u800c\u805a\u7126\u7684\u6846\u67b6\u4e0b\u8ba8\u8bba\u7b97\u6cd5\u7684\u6548\u7387\u4e0e\u53ef\u6269\u5c55\u6027\uff0c\u540c\u65f6\u5ffd\u7565\u5bf9\u4e0d\u91cd\u8981\u751f\u7269\u5b66\u7ec6\u8282\u7684\u8ba1\u7b97\u3002\u8981\u5b9e\u73b0\u795e\u7ecf\u5f62\u6001\u7b97\u6cd5\u7684\u89c4\u6a21\u5316\u8fd8\u9700\u8981\u7279\u522b\u5173\u6ce8\u5f02\u6b65\u548c\u5206\u5e03\u5f0f\u7b97\u6cd5\uff0c\u4f46\u8fd9\u4e00\u65b9\u5411\u7684\u7814\u7a76\u4e3b\u8981\u7531\u4e0d\u540c\u4e8e\u6df1\u5ea6\u5b66\u4e60\u4e0e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u4e3b\u6d41\u793e\u533a\u7684\u7814\u7a76\u7fa4\u4f53\u63a8\u52a8\uff0c\u5c1a\u672a\u5f62\u6210\u7d27\u5bc6\u7684\u534f\u4f5c\u3002\u786e\u5b9a\u5173\u952e\u63a5\u53e3\u9700\u6c42<\/span><\/span>\uff08\u5982\u9ad8\u901f\u4f20\u611f\u5668\u96c6\u6210\u3001\u591a\u79cd\u4f20\u611f\u5668\u517c\u5bb9\u6027\uff09<\/span><\/span>\u540c\u6837\u662f\u7cfb\u7edf\u8bbe\u8ba1\u9636\u6bb5\u5fc5\u987b\u89e3\u51b3\u7684\u91cd\u8981\u6311\u6218\u3002\u6700\u540e\uff0c\u5982\u4f55\u6301\u7eed\u5438\u5f15\u795e\u7ecf\u79d1\u5b66\u5bb6\u53c2\u4e0e\u8de8\u5b66\u79d1\u5408\u4f5c\uff0c\u7279\u522b\u662f\u5728\u795e\u7ecf\u5f62\u6001\u7814\u7a76\u4eba\u5458\u4e3b\u5bfc\u90e8\u5206\u795e\u7ecf\u79d1\u5b66\u5b9e\u9a8c\u7684\u60c5\u51b5\u4e0b\uff0c\u4e5f\u5c06\u662f\u672a\u6765\u9700\u8981\u91cd\u70b9\u89e3\u51b3\u7684\u95ee\u9898\u4e4b\u4e00\u3002<\/span><\/span><\/section>\n

                        <\/span><\/span><\/section>\n
                        \u4ece\u7528\u6237\u7684\u89d2\u5ea6\u6765\u770b\uff0c\u91cd\u5927\u6311\u6218\u5219\u5728\u4e8e\u5982\u4f55\u786e\u5b9a\u9002\u5408\u7684\u5e94\u7528\u9886\u57df\u4ee5\u5145\u5206\u53d1\u6325\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u4f18\u52bf\u3002\u4e00\u4e2a\u6709\u7528\u7684\u7c7b\u6bd4\u662f\u91cf\u5b50\u8ba1\u7b97<\/span><\/span>\uff08Quantum Computing\uff09<\/span><\/span>\u7684\u53d1\u5c55\u3002\u91cf\u5b50\u8ba1\u7b97\u4e4b\u6240\u4ee5\u80fd\u591f\u83b7\u5f97\u6570\u5341\u4ebf\u7f8e\u5143\u7684\u6295\u8d44\uff0c\u90e8\u5206\u539f\u56e0\u5728\u4e8e\u5176\u5b58\u5728\u5f62\u5f0f\u5316\u7684\u590d\u6742\u5ea6\u4f18\u52bf\uff0c\u4f8b\u5982Shor \u7b97\u6cd5\u5728\u6574\u6570\u56e0\u5b50\u5206\u89e3\u95ee\u9898\u4e0a\u7684\u4f18\u52bf\u4f7f\u5f97\u5176\u5728\u5bc6\u7801\u5b66\u9886\u57df\u5177\u6709\u5b9e\u9645\u5f71\u54cd\uff0c\u5373\u91cf\u5b50\u8ba1\u7b97\u7684\u7406\u8bba\u590d\u6742\u5ea6\u8bc1\u660e\u80fd\u591f\u4e3a\u5176\u63d0\u4f9b\u660e\u786e\u7684\u4ef7\u503c\u652f\u6491\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u5c3d\u7ba1\u5df2\u6709\u7814\u7a76\u8868\u660e\u5728\u67d0\u4e9b\u7279\u5b9a\u4efb\u52a1\u4e0a\u795e\u7ecf\u5f62\u6001\u786c\u4ef6\u5728\u6027\u80fd\u6216\u80fd\u8017\u65b9\u9762\u5177\u6709\u4f18\u52bf\uff0c\u4f46\u4ecd\u7f3a\u4e4f\u7c7b\u4f3c\u7684\u5f62\u5f0f\u5316\u590d\u6742\u5ea6\u8bc1\u660e\uff0c\u8fd9\u51e0\u4e4e\u80af\u5b9a\u5f71\u54cd\u4e86\u5bf9\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u786c\u4ef6\u7684\u6295\u8d44\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u91cf\u5b50\u8ba1\u7b97\u7684\u7406\u8bba\u8bc1\u660e\u6bd4\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u201c\u5b58\u5728\u6027\u8bc1\u660e\u201d<\/span><\/span>\uff08\u5373\u4eba\u8111\u7684\u8ba1\u7b97\u80fd\u529b\uff09<\/span><\/span>\u66f4\u5177\u5438\u5f15\u529b\u3002<\/span><\/span><\/section>\n

                        <\/span><\/span><\/section>\n
                        \u8bb8\u591a\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5c55\u73b0\u51fa\u6f5c\u529b\u7684\u4efb\u52a1\uff0c\u4f8b\u5982\u7ec8\u751f\u5b66\u4e60<\/span><\/span>\uff08Lifelong Learning\uff09<\/span><\/span>\u6216\u6301\u7eed\u5b66\u4e60<\/span><\/span>\uff08Continual Learning<\/span><\/span>\uff09<\/span><\/span><\/span>[58,80]\u3001<\/span><\/span>\u5728\u7a00\u758f\u6570\u636e\u73af\u5883\u4e2d\u7684\u5b66\u4e60\u3001\u5728\u566a\u58f0\u548c\u53d8\u5316\u6761\u4ef6\u4e0b\u7684\u9c81\u68d2\u6027\u3001\u57fa\u4e8e\u5b9e\u65f6\u4f20\u611f\u5668\u6570\u636e\u7684\u8d85\u4f4e\u529f\u8017\u673a\u5668\u5b66\u4e60\u7b49\uff0c\u90fd\u96be\u4ee5\u901a\u8fc7\u6570\u5b66\u4e0a\u53ef\u8bc1\u660e\u7684\u65b9\u5f0f\u6765\u62c6\u89e3\u4e3a\u4e00\u7cfb\u5217\u96be\u5ea6\u9012\u589e\u7684\u57fa\u51c6<\/span><\/span>\uff08Benchmark\uff09<\/span><\/span>\u6d4b\u8bd5\u4efb\u52a1\u3002\u6b64\u5916\u8981\u5b9e\u73b0\u95ed\u73af\u57fa\u51c6\u6d4b\u8bd5\uff0c\u795e\u7ecf\u5f62\u6001\u82af\u7247\u9700\u8981\u80fd\u591f\u4e0e\u4f20\u611f\u5668\u548c\u6267\u884c\u5668\u65e0\u7f1d\u8fde\u63a5\uff0c\u56e0\u800c\u8feb\u5207\u9700\u8981\u5efa\u7acb\u6807\u51c6\u5316\u7684\u901a\u4fe1\u534f\u8bae\u3002\u901a\u4fe1\u534f\u8bae\u9700\u8981\u6db5\u76d6\u4ece\u4f4e\u7ea7\u7535\u5b50\u7535\u8def\u5b9e\u73b0\u5230\u66f4\u9ad8\u5c42\uff0c\u786e\u4fdd\u4e0d\u540c\u7c7b\u578b\u7684\u82af\u7247<\/span><\/span>\uff08\u5305\u62ec\u4f20\u611f\u5668\u3001\u5206\u5e03\u5f0f\u81ea\u9002\u5e94\u5904\u7406\u5668\u3001\u591a\u6838\u767e\u4e07\u795e\u7ecf\u5143\u7cfb\u7edf\u7b49\uff09<\/span><\/span>\u80fd\u591f\u65e0\u7f1d\u4ea4\u4e92\uff0c\u4e14\u80fd\u591f\u517c\u5bb9\u975e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\uff0c\u5f62\u6210\u7edf\u4e00\u7684\u8ba1\u7b97\u751f\u6001\u3002<\/span><\/span><\/section>\n

                        <\/span><\/section>\n
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                        <\/span><\/section>\n<\/section>\n
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                        \u68462\uff1a\u786c\u4ef6\/\u8f6f\u4ef6\u534f\u540c\u8bbe\u8ba1\uff08Hardware\/Software Co-Design\uff09<\/span><\/span><\/p>\n


                        <\/span><\/span><\/p>\n

                        \u4ece\u672c\u8d28\u800c\u8a00\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u662f\u4e00\u4e2a\u8de8\u5b66\u79d1\u9886\u57df\uff0c\u5b83\u878d\u5408\u4e86\u795e\u7ecf\u79d1\u5b66\u3001\u8ba1\u7b97\u673a\u79d1\u5b66\u3001\u5de5\u7a0b\u5b66\u548c\u4eba\u5de5\u667a\u80fd\u3002\u7531\u4e8e\u8fd9\u4e9b\u5b66\u79d1\u7684\u7814\u7a76\u65b9\u6cd5\u548c\u6700\u4f73\u5b9e\u8df5\u5404\u4e0d\u76f8\u540c\uff0c\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u7684\u8bbe\u8ba1\u4e5f\u53ef\u4ee5\u91c7\u7528\u591a\u79cd\u65b9\u6cd5\u3002\u4e00\u79cd\u8bbe\u8ba1\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u7684\u65b9\u6cd5\u662f\u4ece\u5927\u8111\u7684\u53ef\u5851\u6027\u548c\u591a\u5c42\u7ea7\u5b66\u4e60\u673a\u5236\u4e2d\u6c72\u53d6\u7075\u611f\uff0c\u5c06\u5176\u8f6c\u5316\u4e3a\u8ba1\u7b97\u6a21\u578b\uff0c\u7136\u540e\u5728\u786c\u4ef6\u548c\u8f6f\u4ef6\u5c42\u9762\u534f\u540c\u5f00\u53d1\uff0c\u6700\u7ec8\u90e8\u7f72\u6210\u7cfb\u7edf\u3002<\/span><\/span><\/section>\n
                          \n
                        1. \n
                          \u81ea\u9876\u5411\u4e0b\uff08Top-Down\uff09\u65b9\u6cd5\uff1a\u7814\u7a76\u4eba\u5458\u5728\u4e0d\u6d89\u53ca\u8be6\u7ec6\u7684\u795e\u7ecf\u5143\u6216\u7a81\u89e6\u6a21\u578b[15,16]\u7684\u524d\u63d0\u4e0b\uff0c\u62bd\u8c61\u51fa\u5927\u8111\u76ae\u5c42\u7684\u5c42\u6b21\u7ed3\u6784\uff0c\u5e76\u5229\u7528\u8fd9\u4e9b\u6a21\u578b\u6765\u6307\u5bfc\u786c\u4ef6\u548c\u7b97\u6cd5\u7684\u8bbe\u8ba1\u3002\u5728\u6b64\u8fc7\u7a0b\u4e2d\u9700\u8981\u8bc6\u522b\u5927\u8111\u4e2d\u7684\u5173\u952e\u8ba1\u7b97\u67b6\u6784\u548c\u8fde\u63a5\u6a21\u5f0f\uff0c\u4ee5\u4fbf\u6784\u5efa\u53ef\u6269\u5c55\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u3002<\/span><\/section>\n<\/li>\n
                        2. \n
                          \u81ea\u5e95\u5411\u4e0a\uff08Bottom-Up\uff09\u65b9\u6cd5\uff1a\u8bbe\u8ba1\u8005\u5229\u7528\u5e95\u5c42\u5668\u4ef6\u7684\u56fa\u6709\u7279\u6027\u6765\u63a8\u52a8\u65b0\u7684\u7b97\u6cd5\u8fdb\u5c55\uff0c\u4ece\u800c\u4f18\u5316\u8ba1\u7b97\u67b6\u6784\u548c\u7cfb\u7edf[145,146]\u3002\u8be5\u65b9\u6cd5\u4f9d\u8d56\u4e8e\u65b0\u5174\u6280\u672f\u7684\u7a81\u7834\uff0c\u4f8b\u5982\u5b58\u5185\u8ba1\u7b97\uff08In-Memory Computing\uff09[147,148,149]\uff0c\u65b0\u578b\u5668\u4ef6\u6280\u672f\uff08Emerging Device Technologies\uff09[150,151]\uff0c\u4f4e\u7cbe\u5ea6\u7b97\u672f\u8ba1\u7b97\uff08Low-Precision Arithmetic\uff09[123,152]\u7b49\u3002<\/span><\/section>\n<\/li>\n<\/ol>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

                          <\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
                          \n
                          \n
                          \n


                          <\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n

                          \u7136\u800c\uff0c\u65e0\u8bba\u91c7\u7528\u81ea\u9876\u5411\u4e0b\u8fd8\u662f\u81ea\u5e95\u5411\u4e0a\u7684\u65b9\u6cd5\uff0c\u90fd\u4f1a\u56e0\u7b97\u6cd5\u8bbe\u8ba1\u8005\u548c\u786c\u4ef6\u67b6\u6784\u5e08\u5bf9\u5f7c\u6b64\u7684\u6280\u672f\u7ec6\u8282\u7f3a\u4e4f\u4e86\u89e3\u800c\u5f15\u5165\u4f4e\u6548\u95ee\u9898\u3002\u6f5c\u5728\u7684\u89e3\u51b3\u65b9\u6848\u662f\u91c7\u7528\u786c\u4ef6\/\u8f6f\u4ef6\u534f\u540c\u8bbe\u8ba1<\/span><\/span>\uff08Co-Design\uff09<\/span><\/span>\u65b9<\/span><\/span>\u6cd5[80,123,153]\uff0c\u5373\u65e0\u8bba<\/span><\/span>\u91c7\u7528\u4f55\u79cd\u8bbe\u8ba1\u8303\u5f0f\uff0c\u5728\u6574\u4e2a\u8bbe\u8ba1\u8fc7\u7a0b\u4e2d\u90fd\u59cb\u7ec8\u8003\u8651\u7cfb\u7edf\u7684\u53ef\u6269\u5c55\u6027<\/span><\/span>\uff08Scalability\uff09<\/span><\/span>\u4ee5\u63d0\u9ad8\u786c\u4ef6\u4e0e\u8f6f\u4ef6\u7684\u5339\u914d\u5ea6\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n

                          \u8de8\u5e73\u53f0\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u8f6f\u4ef6<\/span><\/span><\/strong><\/span><\/h3>\n

                          <\/span><\/span><\/section>\n
                          \u4e3b\u6d41\u6df1\u5ea6\u5b66\u4e60\u7684\u6210\u529f\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u5f52\u56e0\u4e8e\u5f3a\u5927\u4e14\u6613\u7528\u7684\u5f00\u6e90\u8f6f\u4ef6\u5de5\u5177\u7684\u53ef\u7528\u6027\u3002\u8fd9\u4e9b\u5de5\u5177\u4e3a\u7528\u6237\u548c\u5e94\u7528\u5f00\u53d1\u8005\u63d0\u4f9b\u4e86\u9ad8\u5ea6\u62bd\u8c61\u7684\u7f16\u7a0b\u63a5\u53e3\uff0c\u4f7f\u4ed6\u4eec\u65e0\u9700\u6df1\u5165\u7406\u89e3\u673a\u5668\u5b66\u4e60\u7684\u7406\u8bba\u7ec6\u8282\u5373\u53ef\u4f7f\u7528\u3002\u4f8b\u5982TensorFlow\u3001Keras\u548c PyTorch\u7b49\u6846\u67b6\u5c4f\u853d\u4e86\u5e95\u5c42\u6570\u5b66\u8ba1\u7b97\u7684\u590d\u6742\u6027<\/span><\/span>\uff08\u5982\u81ea\u52a8\u5fae\u5206\uff09<\/span><\/span>\uff0c\u8ba9\u7528\u6237\u80fd\u591f\u4e13\u6ce8\u4e8e\u9ad8\u5c42\u6b21\u7684\u7f51\u7edc\u7ed3\u6784\uff0c\u800c\u4e0d\u5fc5\u76f4\u63a5\u5904\u7406\u4f4e\u7ea7\u8ba1\u7b97\u7ec6\u8282\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5728\u8fd9\u4e00\u65b9\u9762\u4ecd\u7136\u5b58\u5728\u660e\u663e\u4e0d\u8db3\u3002\u5f53\u524d\u53ef\u5373\u63d2\u5373\u7528\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5de5\u5177\u4ecd\u975e\u5e38\u6709\u9650\uff0c\u5e76\u4e14\u4e3b\u8981\u7531\u5c11\u6570\u7814\u7a76\u4eba\u5458\u5f00\u53d1\u548c\u7ef4\u62a4\uff0c\u4f8b\u5982 jAE<\/span><\/span>R[81]<\/span><\/span> (Jav<\/span><\/span>a\u7248\u7684\u5730\u5740\u4e8b\u4ef6\u8868\u793a\u6846\u67b6\uff09<\/span><\/span><\/span>\u3002\u5c3d\u7ba1\u793e\u533a\u9a71\u52a8\u7684\u652f\u6301\u548c\u5f00\u6e90\u5de5\u5177\u7684\u53d1\u5c55\u5177\u6709\u6f5c\u529b\uff0c\u4f46\u5982\u4f55\u5b9a\u4e49\u9002\u5f53\u7684\u62bd\u8c61\u5c42\u7ea7\u4ee5\u5f00\u53d1\u8fd9\u4e9b\u8ba1\u7b97\u5de5\u5177\u4ecd\u7136\u662f\u4e00\u4e2a\u5f00\u653e\u6027\u95ee\u9898\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n
                          \u76ee\u524d\uff0c\u9762\u5411\u795e\u7ecf\u5f62\u6001\u786c\u4ef6\u7684\u795e\u7ecf\u7f51\u7edc\u5efa\u6a21\u5de5\u5177\u4ecd\u7136\u76f8\u5bf9\u843d\u540e\uff0c\u5176\u5f00\u53d1\u6c34\u5e73\u5927\u81f4\u76f8\u5f53\u4e8e\u5bc4\u5b58\u5668\u4f20\u8f93\u7ea7\u8bed\u8a00<\/span><\/span>\uff08RTL, Register Transfer Language\uff09<\/span><\/span>\uff0c\u800c\u975e\u9ad8\u5c42\u6b21\u7efc\u5408<\/span><\/span>\uff08HLS, High-Level Synthesis\uff09<\/span><\/span>\u3002\u8fd9\u4e00\u6210\u719f\u5ea6\u4e0d\u8db3\u7684\u539f\u56e0\u4f8b\u5982\u7814\u7a76\u793e\u533a\u7684\u53d1\u5c55\u65b9\u5411\u5206\u6563\uff0c<\/span><\/span>\u5404\u56e2\u961f\u91c7\u7528\u4e0d\u540c\u7684\u65b9\u6cd5\uff1b\u7b97\u6cd5\u591a\u6837\u6027\u8f83\u9ad8\uff0c\u7f3a\u4e4f\u7edf\u4e00\u89c4\u8303\uff1b\u4e0d\u540c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5e73\u53f0<\/span><\/span>\uff08\u6a21\u62df[82]\u3001\u6570\u5b57[23]\u6216\u8f6f\u4ef6\u5b9e\u73b0[4]\uff09<\/span><\/span>\u5404\u81ea\u5177\u6709\u72ec\u7279\u7684\u5b9e\u73b0\u9700\u6c42\uff0c\u5bfc\u81f4\u901a\u7528\u5de5\u5177\u7684\u5f00\u53d1\u53d8\u5f97\u56f0\u96be\u3002\u8fd1\u5e74\u6765\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u6846\u67b6\u7684\u529f\u80fd\u6709\u6240\u589e\u5f3a\uff0c\u80fd\u591f\u652f\u6301\u591a\u79cd\u795e\u7ecf\u5143\u6a21\u578b\u3001\u4e0d\u540c\u7684\u5b66\u4e60\u89c4\u5219\u3001\u66f4\u4e30\u5bcc\u7684\u540e\u7aef\u8ba1\u7b97\u5e73\u53f0\u4ee5\u53ca\u4ece\u4e3b\u6d41\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6<\/span><\/span>\uff08\u5982 TensorFlow\u3001PyTorch\uff09<\/span><\/span>\u5bfc\u5165\u6a21\u578b[59]\u3002\u7136\u800c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u6846\u67b6\u5728\u7814\u7a76\u793e\u533a\u7684\u6709\u9650\u5e94\u7528\uff0c\u5e76\u975e\u4ec5\u7531\u4e8e\u7f3a\u4e4f\u5de5\u5177\uff0c\u800c\u8fd8\u5728\u4e8e\u7f3a\u4e4f\u7531\u793e\u533a\u4e3b\u5bfc\u7684\u5171\u4eab\u6807\u51c6\u3002\u5f53\u524d\u8bb8\u591a\u5de5\u5177\u7684\u5f00\u53d1\u4ecd\u7136\u53d7\u9650\u4e8e\u7279\u5b9a\u5546\u4e1a\u673a\u6784\u7684\u4e3b\u5bfc\uff0c\u800c\u4e0d\u662f\u7531\u6574\u4e2a\u7814\u7a76\u793e\u533a\u5171\u540c\u63a8\u8fdb\uff0c\u8fd9\u963b\u788d\u4e86\u5de5\u5177\u7684\u5e7f\u6cdb\u91c7\u7528\u548c\u751f\u6001\u7edf\u4e00\u3002\u8fd1\u671f\uff0c\u5f00\u6e90\u6846\u67b6\u548c\u901a\u7528\u4e2d\u95f4\u8868\u793a<\/span><\/span>\uff08Intermediate Representation, IR\uff09<\/span><\/span>\u7684\u5efa\u7acb[52]\u4e3a\u63a8<\/span><\/span>\u52a8\u6807\u51c6\u5316\u8fc8\u51fa\u4e86\u91cd\u8981\u4e00\u6b65\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n
                          \u7814\u7a76\u793e\u533a\u5bf9\u5c11\u91cf\u6838\u5fc3\u8f6f\u4ef6\u6846\u67b6\u7684\u5e7f\u6cdb\u63a5\u53d7\uff0c\u4f7f\u5f97\u786c\u4ef6\u516c\u53f8\u80fd\u591f\u4e13\u6ce8\u4e8e\u5f00\u53d1\u5c16\u7aef\u52a0\u901f\u5668\uff0c\u800c\u65e0\u9700\u4e3a\u6bcf\u6b3e\u65b0\u82af\u7247\u91cd\u65b0\u8bbe\u8ba1\u5b8c\u6574\u7684\u8f6f\u4ef6\u6808\u3002\u8fd9\u4e00\u4f18\u5316\u53cd\u8fc7\u6765\u4e5f\u4fc3\u8fdb\u4e86\u65b0\u82af\u7247\u7684\u91c7\u7528\uff0c\u63a8\u52a8\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001\u7684\u8fdb\u4e00\u6b65\u53d1\u5c55\u3002\u672a\u6765\uff0c\u6211\u4eec\u9884\u8ba1\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5de5\u5177\u5c06\u671d\u7740\u66f4\u9ad8\u5c42\u6b21\u7684\u62bd\u8c61\u53d1\u5c55\uff0c\u5305\u62ec\u91c7\u7528\u901a\u7528\u7684\u4e2d\u95f4\u8868\u793a\u5c42\uff0c\u589e\u5f3a\u8de8\u5e73\u53f0\u517c\u5bb9\u6027\uff0c\u4ee5\u53ca\u4ece\u529f\u80fd\u6a21\u5757\u5e93\u4e2d\u7ec4\u88c5\u5e94\u7528\u7a0b\u5e8f\uff0c\u5176\u4e2d\u6bcf\u4e2a\u6a21\u5757\u7531\u8109\u51b2\u795e\u7ecf\u5143<\/span><\/span>\uff08Spiking Neurons\uff09<\/span><\/span>\u7ec4\u6210\u3002\u7136\u800c\u8fd9\u4e9b\u529f\u80fd\u6a21\u5757\u5e94\u5177\u5907\u54ea\u4e9b\u7279\u6027\u3001\u5982\u4f55\u5f00\u53d1\u8fd9\u4e9b\u6a21\u5757\uff0c\u76ee\u524d\u4ecd\u7136\u662f\u4e00\u4e2a\u5f00\u653e\u6027\u95ee\u9898\uff0c\u9700\u8981\u8fdb\u4e00\u6b65\u7814\u7a76\u548c\u63a2\u7d22\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n


                          <\/span><\/span><\/p>\n

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                          <\/span><\/section>\n

                          <\/span><\/section>\n
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                          <\/section>\n<\/section>\n<\/section>\n
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                          \u793e\u533a\u51c6\u5907\u5ea6\u4e0e\u751f\u6001<\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n

                          \n

                          <\/span><\/section>\n

                          <\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n


                          <\/span><\/p>\n

                          \u5728\u6210\u719f\u4f01\u4e1a\u548c\u521d\u521b\u516c\u53f8\u79ef\u6781\u53c2\u4e0e\u6240\u5f62\u6210\u7684\u589e\u957f\u578b\u751f\u6001\u4e0b\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u9886\u57df\u6709\u671b\u4ece\u957f\u671f\u7684\u57fa\u7840\u7814\u7a76\u79ef\u7d2f\u4ee5\u53ca\u65e5\u76ca\u589e\u957f\u7684\u5546\u4e1a\u5174\u8da3\u4e2d\u53d7\u76ca\u3002\u5c3d\u7ba1\u5f53\u524d\u5df2\u6709\u7cfb\u7edf\u80fd\u591f\u7cbe\u786e\u6a21\u62df\u5927\u8111\u7684\u81ea\u5e95\u5411\u4e0a\u8ba1\u7b97\u8fc7\u7a0b\uff0c\u4f46\u5927\u591a\u6570\u7814\u7a76\u8005\u548c\u4e1a\u754c\u4eba\u58eb\u4ecd\u4e0d\u613f\u610f\u653e\u5f03\u73b0\u6709\u89e3\u51b3\u65b9\u6848\uff0c\u9664\u975e\u4ed6\u4eec\u80fd\u591f\u6e05\u6670\u5730\u770b\u5230\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5e26\u6765\u7684\u4f18\u52bf\u3002\u5728\u6b64\uff0c\u6211\u4eec\u6982\u8ff0\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001\u7cfb\u7edf\u7684\u5173\u952e\u7279\u5f81\uff0c\u5e76\u63d0\u51fa\u4e00\u7cfb\u5217\u4fc3\u8fdb\u7814\u7a76\u793e\u533a\u4e3a\u5927\u89c4\u6a21\u7cfb\u7edf\u53d1\u5c55\u505a\u597d\u51c6\u5907\u7684\u8003\u91cf\u56e0\u7d20\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n

                          \u7814\u53d1\u56e2\u961f\uff08R&D Groups\uff09<\/span><\/span><\/strong><\/h3>\n

                          <\/span><\/span><\/section>\n
                          \u5982<\/span><\/span>\u56fe5\u6240<\/span><\/span>\u793a\uff0c\u4e3a\u7814\u53d1\u56e2\u961f\u63d0\u4f9b<\/span><\/span>\u65e9\u671f\u4e14\u4fbf\u6377\u7684\u6280\u672f\u8bbf\u95ee\u6e20\u9053\uff0c\u5c24\u5176\u662f\u8d85\u8d8a\u4f20\u7edf\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001\u7cfb\u7edf\u7684\u7814\u7a76\u7fa4\u4f53\uff0c\u80fd\u591f\u5f62\u6210\u6280\u672f\u63a8\u5e7f\u7684\u4e58\u6570\u6548\u5e94\uff0c\u52a0\u901f\u8be5\u65b0\u5174\u8ba1\u7b97\u8303\u5f0f\u7684\u4f18\u52bf\u4f20\u64ad\uff0c\u4f8b\u5982\u4f4e\u80fd\u8017\u548c\u5c0f\u5c3a\u5bf8\u7b49\u4f18\u52bf\u7279\u6027\u3002\u65e9\u671f\u6280\u672f\u8bbf\u95ee\u6307\u5411\u5e7f\u6cdb\u7684\u5229\u76ca\u76f8\u5173\u65b9\u5f00\u653e\u786c\u4ef6\u539f\u578b<\/span><\/span>\uff08Hardware Prototypes\uff09<\/span><\/span>\uff0c\u4f7f\u4ed6\u4eec\u80fd\u591f\u6d4b\u8bd5\u548c\u9a8c\u8bc1\u8be5\u6280\u672f\u3002\u6b64\u5916\uff0c\u4e3a\u4e86\u4fdd\u6301\u7814\u7a76\u793e\u533a\u7684\u6301\u7eed\u53c2\u4e0e\u5ea6\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7acb\u5956\u9879\u3001\u7ade\u8d5b\u3001\u7edf\u4e00\u7684\u57fa\u51c6\u6d4b\u8bd5\u3001\u65b0\u6210\u5458\u5165\u95e8\u6307\u5bfc\u3001\u957f\u671f\u6280\u672f\u652f\u6301\u3001\u4e13\u9898\u7814\u8ba8\u4f1a\u7b49\u63aa\u65bd\u6fc0\u52b1\u7814\u53d1\u56e2\u961f\u3002\u76ee\u524d\u5df2\u6709\u591a\u4e2a\u793e\u533a\u63a8\u52a8\u7684\u6d3b\u52a8\u5728\u63a8\u8fdb\u8fd9\u4e9b\u52aa\u529b\uff0c\u5305\u62ecTelluride \u548c CapoCaccia \u7814\u8ba8\u4f1a\u3001NICE<\/span><\/span>\uff08\u795e\u7ecf\u4fe1\u606f\u5904\u7406\u4e0e\u8ba1\u7b97\u5de5\u7a0b\u4f1a\u8bae\uff09<\/span><\/span>\u3001ICONS<\/span><\/span>\uff08\u56fd\u9645\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u4f1a\u8bae\uff09<\/span><\/span>\u3001Intel INRC<\/span><\/span>\uff08Intel \u795e\u7ecf\u5f62\u6001\u7814\u7a76\u793e\u533a\uff09<\/span><\/span>\u7b49\u3002\u8fd9\u4e9b\u7814\u7a76\u6d3b\u52a8\u4e0e\u5408\u4f5c\u5e73\u53f0\u6709\u52a9\u4e8e\u52a0\u5f3a\u793e\u533a\u5efa\u8bbe\uff0c\u5e76\u63a8\u52a8\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u5e7f\u6cdb\u91c7\u7528\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n

                          \u6613\u7528\u901a\u7528\u7684\u5f00\u6e90\u8f6f\u4ef6\uff08Easy, Common and Open-Source Software\uff09<\/span><\/span><\/strong><\/h3>\n

                          <\/span><\/span><\/section>\n
                          \u6b63\u5982\u524d\u6587\u201c\u8de8\u5e73\u53f0\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u8f6f\u4ef6\u201d\u90e8\u5206\u6240\u8ba8\u8bba\u7684\uff0c\u6613\u7528\u6027\u5728\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u8f6f\u4ef6\u751f\u6001\u4e2d\u7684\u6838\u5fc3\u4f5c\u7528\u4e3b\u8981\u4f53\u73b0\u5728\u6613\u7528\u7684\u8f6f\u4ef6\u8bbf\u95ee<\/span><\/span>\uff08Easy Software Access\uff09<\/span><\/span>\uff0c\u5373\u5f00\u53d1\u7f16\u8bd1\u5668<\/span><\/span>\uff08Compilers\uff09<\/span><\/span>\u4f7f\u4e3b\u6d41\u7528\u6237\u80fd\u591f\u76f4\u63a5\u90e8\u7f72\u6a21\u578b\u81f3\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5e73\u53f0\uff0c\u800c\u65e0\u9700\u7406\u89e3\u5e95\u5c42\u786c\u4ef6\u7ec6\u8282\u3002\u5e94\u8be5\u5f00\u53d1\u901a\u7528\u8de8\u7f16\u8bd1\u5668\uff0c\u4ee5\u4fbf\u8f7b\u677e\u96c6\u6210\u65b0\u7684\u540e\u7aef<\/span><\/span>\uff08Back-Ends\uff09<\/span><\/span>\uff0c\u4fc3\u8fdb\u786c\u4ef6\u5382\u5546\u9075\u5faa\u7edf\u4e00\u6807\u51c6\u3002\u91c7\u7528\u901a\u7528\u3001\u5e7f\u6cdb\u63a5\u53d7\u7684\u8f6f\u4ef6\u6846\u67b6<\/span><\/span>\uff08Common and Accepted Frameworks\uff09<\/span><\/span>\u6709\u52a9\u4e8e\u964d\u4f4e\u786c\u4ef6\u5382\u5546\u7684\u4e2a\u4f53\u5f00\u53d1\u6210\u672c\uff0c\u907f\u514d\u91cd\u590d\u9020\u8f6e\u5b50\u3002\u5728\u8f6f\u4ef6\u6808\u7684\u5404\u4e2a\u5c42\u7ea7\u4f7f\u7528\u5bbd\u677e\u7684\u5f00\u6e90\u8bb8\u53ef\u534f\u8bae<\/span><\/span>\uff08\u5982 Apache License 2.0\uff09<\/span><\/span>\u5219\u53ef\u4ee5\u9f13\u52b1\u793e\u533a\u8d21\u732e\uff0c\u4fc3\u8fdb\u751f\u6001\u5408\u4f5c\uff0c\u5e76\u6d88\u9664\u5546\u4e1a\u516c\u53f8\u7684\u987e\u8651\uff0c\u8ba9\u5b83\u4eec\u5728\u4e0d\u53d7\u4e25\u683c\u9650\u5236\u7684\u524d\u63d0\u4e0b\u79ef\u6781\u53c2\u4e0e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u8f6f\u4ef6\u7684\u53d1\u5c55\uff0c\u63a8\u52a8\u6280\u672f\u7684\u5b9e\u9645\u843d\u5730\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n

                          \u57fa\u51c6\u6d4b\u8bd5\u5e73\u53f0\uff08Benchmarks\uff09<\/span><\/span><\/strong><\/h3>\n

                          <\/span><\/span><\/section>\n
                          \u7814\u7a76\u793e\u533a\u5e7f\u6cdb\u63a5\u53d7\u7684\u8f6f\u4ef6\u6808\u5bf9\u901a\u7528\u8fdb\u5c55\u8861\u91cf\u6807\u51c6\u7684\u91c7\u7528\u5177\u6709\u76f4\u63a5\u5f71\u54cd\u3002\u7136\u800c\uff0c\u7531\u4e8e\u8be5\u9886\u57df\u5c1a\u672a\u5b8c\u5168\u6210\u719f\uff0c\u5e76\u4e14\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u672c\u8d28\u4e0a\u5177\u6709\u5f02\u6784\u6027\uff0c\u5728\u5b9a\u4e49\u6807\u51c6\u5316\u7684\u5ea6\u91cf\u6307\u6807<\/span><\/span>\uff08Metrics\uff09<\/span><\/span>\u548c\u57fa\u51c6\u6d4b\u8bd5\u65b9\u9762\u9762\u4e34\u8bf8\u591a\u6311\u6218\u3002\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u7684\u591a\u6837\u6027\u5bfc\u81f4\u7b97\u6cd5\u548c\u786c\u4ef6\u5e73\u53f0\u4e4b\u95f4\u96be\u4ee5\u76f4\u63a5\u6bd4\u8f83\uff0c\u56e0\u6b64\u9700\u8981\u591a\u7ef4\u5ea6\u7684\u5ea6\u91cf\u6307\u6807\u548c\u591a\u6837\u5316\u7684\u57fa\u51c6\u6d4b\u8bd5\u3002\u76ee\u524d\u5df2\u6709\u90e8\u5206\u6280\u672f\u5df2\u7ecf\u6210\u719f\uff0c\u4f8b\u5982\u57fa\u4e8e\u4e8b\u4ef6\u9a71\u52a8\u7684\u63a5\u53e3\u6807\u51c6\u3001I\/O \u795e\u7ecf\u5143\u7b49\uff0c\u5bf9\u5176\u8fdb\u884c\u6b63\u5f0f\u89c4\u8303\u5316\u53ef\u4ee5\u4e3a\u516c\u5e73<\/span><\/span>\u7684\u57fa\u51c6\u6d4b\u8bd5\u5960\u5b9a\u57fa\u7840\u3002\u786c\u4ef6\u65e0\u5173\u7684\u8f6f\u4ef6\u6808\u53ef\u4ee5\u4f5c\u4e3a\u5efa\u7acb\u901a\u7528\u57fa\u51c6\u6d4b\u8bd5\u7684\u53c2\u8003\u67b6\u6784\u3002\u73b0\u6709\u7684\u57fa\u51c6\u6d4b\u8bd5\u793a\u4f8b\u5305\u62ec\uff1a\u53d7\u9ad8\u6027\u80fd\u8ba1\u7b97\u542f\u53d1\u7684\u7cfb\u7edf\u7ea7\u57fa\u51c6\u6d4b\u8bd5[83]\u3001\u7531\u5de5\u4e1a\u754c\u4e3b\u5bfc\u7684\u8109\u51b2\u795e\u7ecf\u7f51\u7edc<\/span><\/span>\uff08Spiking Systems\uff09<\/span><\/span>\u57fa\u51c6\u6d4b\u8bd5[84]\u548c\u7528\u4e8e\u6982\u7387\u7cfb\u7edf<\/span><\/span>\uff08Probabilistic Systems\uff09<\/span><\/span>\u7684\u57fa\u51c6\u6d4b\u8bd5\u5de5\u5177[85,86]\u3002\u6570\u636e\u96c6\u65b9\u9762\uff0c\u5df2\u6709\u591a\u4e2a\u4e13\u95e8\u7528\u4e8e\u8bc4\u4f30\u8109\u51b2\u795e\u7ecf\u7f51\u7edc<\/span><\/span>\uff08SNN\uff09<\/span><\/span>\u6027\u80fd\u7684\u6570\u636e\u96c6[87,88,89,90,91,92,93]\uff0c\u4f46\u8fd9\u4e9b\u6570\u636e\u96c6\u5c1a\u672a\u88ab\u5e7f\u6cdb\u91c7\u7528[30]\u3002\u786c\u4ef6\u6027\u80fd\u5ea6\u91cf\u65b9\u9762\uff0c\u76ee\u524d\u5c1a\u65e0\u7814\u7a76\u793e\u533a\u8fbe\u6210\u5171\u8bc6\u7684\u7edf\u4e00\u6307\u6807\u3002\u5e38\u89c1\u7684\u5355\u4e00\u5ea6\u91cf\u6807\u51c6\u5305\u62ec\u80fd\u8017\u3001\u529f\u7387\u3001\u5ef6\u8fdf<\/span><\/span>\uff08Latency\uff09<\/span><\/span>\u3001\u51c6\u786e\u7387<\/span><\/span>\uff08Accuracy\uff09<\/span><\/span>\u3001\u82af\u7247\u9762\u79ef\u3001\u9c81\u68d2\u6027<\/span><\/span>\uff08Robustness\uff09<\/span><\/span>\u3001\u566a\u58f0\u5bb9\u5fcd\u5ea6<\/span><\/span>\uff08Noise Tolerance\uff09<\/span><\/span>\u7b49\u3002\u4f46\u5355\u72ec\u4f7f\u7528\u8fd9\u4e9b\u5ea6\u91cf\u6307\u6807\u901a\u5e38\u53ea\u6709\u5728\u6bd4\u8f83\u9ad8\u5ea6\u76f8\u4f3c\u7684\u8bbe\u5907\u6216\u7cfb\u7edf\u65f6\u624d\u5177\u6709\u5b9e\u9645\u610f\u4e49\u3002\u56e0\u6b64\uff0c\u7814\u7a76\u4eba\u5458\u66f4\u503e\u5411\u4e8e\u91c7\u7528\u590d\u5408\u6307\u6807\uff0c\u4f8b\u5982\u80fd\u91cf\u5ef6\u8fdf\u4e58\u79ef<\/span><\/span>\uff08Energy-Delay Product\uff09<\/span><\/span>\u3001\u6bcf\u4e2a\u7a81\u89e6\u64cd\u4f5c\u7684\u80fd\u8017<\/span><\/span>\uff08Energy per Synapse\uff09<\/span><\/span>\u3001\u76f8\u5bf9\u51c6\u786e\u7387<\/span><\/span>\uff08Relative Accuracy\uff09<\/span><\/span>\u7b49\u3002\u5c3d\u7ba1\u5982\u6b64\uff0c\u6807\u51c6\u3001\u5ea6\u91cf\u6307\u6807\u548c\u57fa\u51c6\u6d4b\u8bd5\u7684\u5f00\u53d1\u4ecd\u7136\u662f\u4e00\u4e2a\u5173\u952e\u4e14\u6d3b\u8dc3\u7684\u7814\u7a76\u9886\u57df\uff0c\u5176\u76ee\u6807\u662f\u5b9e\u73b0\u672a\u6765\u7684\u5e7f\u6cdb\u91c7\u7528\u3002\u76ee\u524d\u7814\u7a76\u793e\u533a\u6b63\u5728\u52aa\u529b\u6539\u8fdb\u73b0\u72b6\uff0c\u901a\u8fc7\u5b9a\u4e49\u4e00\u7cfb\u5217\u57fa\u51c6\u6d4b\u8bd5\u548c\u76f8\u5173\u5ea6\u91cf\u6307\u6807\uff0c\u4ee5\u6bd4\u8f83\u4e0d\u540c\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7b97\u6cd5\u548c\u786c\u4ef6\u7cfb\u7edf[94]\u3002\u6b64\u5916\uff0c\u8fd8\u5f00\u53d1\u4e86\u7528\u4e8e\u8bc4\u4f30\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u6027\u80fd\u7684\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u795e\u7ecf\u79d1\u5b66\u5efa\u6a21\u548c\u5fc3\u7406\u5b66\u5efa\u6a21[95,96]\u3002\u7136\u800c\uff0c\u8981\u5b9a\u4e49\u4e00\u4e2a\u901a\u7528\u4e8e\u6240\u6709\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u5355\u4e00\u57fa\u51c6<\/span><\/span>\u6d4b\u8bd5\u548c\u5ea6\u91cf\u6807\u51c6\u4ecd\u7136\u662f\u4e00\u9879\u5de8\u5927\u6311\u6218\uff0c\u7279\u522b\u662f\u9700\u8981\u8003\u8651\u5230\u201c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5173\u952e\u7279\u6027\u201d\u90e8\u5206\u6240\u63cf\u8ff0\u7684\u5404\u79cd\u7cfb\u7edf\u7279\u6027\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n

                          \u8de8\u9886\u57df\u6280\u672f\uff08Field-Crossing Technology\uff09<\/span><\/span><\/strong><\/h3>\n

                          <\/span><\/span><\/section>\n
                          \u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u5728\u4f20\u7edf\u8ba1\u7b97\u4e0e\u795e\u7ecf\u5f62\u6001\u5e94\u7528\u7a0b\u5e8f\u63a5\u53e3<\/span><\/span>\uff08API\uff09<\/span><\/span>\u4e4b\u95f4\u7684\u53cc\u5411\u8f6c\u6362\u8fc7\u7a0b\u4e2d\uff0c\u5fc5\u987b\u5e94\u5bf9\u989d\u5916\u7684\u63a5\u53e3\u635f\u8017<\/span><\/span>\uff08Interface Losses\uff09<\/span><\/span>\u3002\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u8fd9\u79cd\u635f\u8017\u6e90\u4e8e\u8109\u51b2\u7f16\u7801\u65b9\u6848\u7684\u8f6c\u6362\u95ee\u9898\u3002\u89e3\u51b3\u8fd9\u4e9b\u63a5\u53e3\u635f\u8017\u95ee\u9898\u81f3\u5173\u91cd\u8981\uff0c\u5426\u5219\u5373\u4f7f\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5f15\u64ce\u5177\u5907\u9ad8\u6548\u7684\u8ba1\u7b97\u80fd\u529b\uff0c\u5176\u6574\u4f53\u4f18\u52bf\u4ecd\u53ef\u80fd\u56e0\u63a5\u53e3\u6548\u7387\u4f4e\u4e0b\u800c\u88ab\u62b5\u6d88\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n

                          \u6982\u5ff5\u9a8c\u8bc1\uff08Proofs of Concept, PoC\uff09<\/span><\/span><\/strong><\/h3>\n

                          <\/span><\/span><\/section>\n
                          \u884c\u4e1a\u7279\u5b9a\u7684\u6982\u5ff5\u9a8c\u8bc1\u662f\u7814\u7a76\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u7684\u80fd\u6548\u548c\u6027\u80fd\u4f18\u52bf\u7684\u91cd\u8981\u624b\u6bb5\u3002\u4e3a\u4e86\u6700\u5927\u5316\u6295\u8d44\u7684\u5f71\u54cd\u529b\uff0c\u5bf9\u4e8e\u4efb\u4f55\u65b0\u6280\u672f\u800c\u8a00\u9009\u62e9\u5408\u9002\u7684\u6982\u5ff5\u9a8c\u8bc1\u65b9\u5411\u90fd\u81f3\u5173\u91cd\u8981\u3002\u6982\u5ff5\u9a8c\u8bc1\u7684\u5f71\u54cd\u529b\u66f4\u5927\u65f6\uff0c\u5f80\u5f80\u80fd\u591f\u5728\u7279\u5b9a\u9886\u57df\u4ea7\u751f\u201c\u6d9f\u6f2a\u6548\u5e94\u201d<\/span><\/span>\uff08Ripple Effect\uff09<\/span><\/span>\u3002\u4f8b\u5982\uff0c\u8bbe\u8ba1\u4e00\u4e2a\u9762\u5411\u7ec8\u7aef\u7528\u6237\u7684\u80fd\u6548\u4f18\u5316\u6982\u5ff5\u9a8c\u8bc1\uff0c\u5176\u5f71\u54cd\u529b\u53ef\u80fd\u8fdc\u4e0d\u53ca\u8bbe\u8ba1\u4e00\u4e2a\u9762\u5411\u5927\u578b\u5bf9\u8bdd\u5f15\u64ce\u6280\u672f\u670d\u52a1\u63d0\u4f9b\u5546\u7684\u6982\u5ff5\u9a8c\u8bc1\u3002\u6280\u672f\u670d\u52a1\u63d0\u4f9b\u5546\u76f4\u63a5\u53d7\u5230\u80fd\u8017\u7684\u5f71\u54cd\uff0c\u56e0\u6b64\u4ed6\u4eec\u7684\u8ba4\u53ef\u548c\u91c7\u7528\u80fd\u591f\u63a8\u52a8\u66f4\u5927\u8303\u56f4\u7684\u7ec8\u7aef\u7528\u6237\u53d7\u76ca\u3002\u5728\u6982\u5ff5\u9a8c\u8bc1\u9636\u6bb5\u6784\u5efa\u5927\u89c4\u6a21\u539f\u578b\u800c\u975e\u91c7\u7528\u6e10\u8fdb\u5f0f\u5f00\u53d1\u65b9\u5f0f\uff0c\u5c06\u5e26\u6765\u66f4\u5927\u7684\u5b9e\u9645\u6536\u76ca\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n

                          \u503e\u542c\u53cd\u9988\uff08Listening to the Feedback\uff09<\/span><\/span><\/strong><\/h3>\n

                          <\/span><\/span><\/section>\n
                          \u9012\u5f52\u5f0f\u53cd\u9988\u5faa\u73af<\/span><\/span>\uff08Recursive Feedback Loop\uff09<\/span><\/span>\u6709\u52a9\u4e8e\u6539\u8fdb\u7814\u7a76\u65b9\u6cd5\u3001\u4f18\u5316\u6982\u5ff5\u9a8c\u8bc1\uff0c\u5e76\u8bc4\u4f30\u6280\u672f\u7684\u91c7\u7528\u7a0b\u5ea6\u3002\u7ef4\u6301\u4ee5\u7528\u6237\u4e3a\u4e2d\u5fc3\u7684\u65b9\u6cd5\u80fd\u591f\u4fc3\u4f7f\u81ea\u5e95\u5411\u4e0a\u7684\u65b9\u6cd5\u8bba\u8003\u8651\u5b9e\u9645\u5e94\u7528\u9700\u6c42\uff0c\u5e76\u63a8\u52a8\u786c\u4ef6\/\u8f6f\u4ef6\u7684\u534f\u540c\u8bbe\u8ba1<\/span><\/span>\uff08Hardware\/Software Co-Design\uff09<\/span><\/span>\u3002<\/span><\/span><\/section>\n

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                          <\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n


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                          \u795e\u7ecf\u79d1\u5b66\u63a2\u7d22 Neuroscience exploration<\/span><\/span><\/strong><\/h3>\n

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                          \u5728\u8fc8\u5411\u8111\u89c4\u6a21\u5316\u6a21\u62df\u7684\u8fc7\u7a0b\u4e2d\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u5e94\u7528\u53d8\u5f97\u5c24\u4e3a\u91cd\u8981\u3002\u4e00\u4e2a\u5178\u578b\u6848\u4f8b\u662f\u865a\u62df\u8111<\/span><\/span>\uff08The Virtual Brain, TVB\uff09<\/span><\/span>[97]\uff0c\u4e00\u4e2a\u5305\u542b\u590d\u6742\u751f\u7269\u5b66\u7ec6\u8282\u7684\u8111\u7ea7\u522b\u6a21\u578b\uff0c\u4f46\u5728GPU\u4e0a\u8fdb\u884c\u5982\u6b64\u89c4\u6a21\u7684\u5b9e\u65f6\u6a21\u62df\u4ecd\u7136\u6781\u5177\u6311\u6218\u6027\u3002\u865a\u62df\u8111\u6a21\u578b\u5728\u533b\u5b66\u9886\u57df\u5177\u6709\u91cd\u8981\u5e94\u7528\uff0c\u8bf8\u5982\u4e2a\u6027\u5316\u963f\u5c14\u8328\u6d77\u9ed8\u75c5<\/span><\/span>\uff08Alzheimer\u2019s Disease\uff09<\/span><\/span>\u68c0\u6d4b[98]\uff0c\u80fd\u591f\u52a0\u901f\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u5728\u533b<\/span><\/span>\u7597\u8bca\u65ad\u4e2d\u7684\u5e94\u7528\uff0c\u5b9e\u73b0\u5feb\u901f\u6267\u884c\uff0c\u63d0\u9ad8\u68c0\u6d4b\u6548\u7387\u3002\u53e6\u4e00\u4e2a\u4f8b\u5b50\u662fMarkram \u7ec6\u80de\u7814\u7a76<\/span><\/span>\uff08Markram Cell Study\uff09<\/span><\/span>\uff0c\u8be5\u7814\u7a76\u4f7f\u7528Blue Brain IV \u8d85\u7ea7\u8ba1\u7b97\u673a\u8fdb\u884c\u6a21\u62df\u3002\u5728\u5f53\u65f6Blue Brain IV \u662f\u5168\u7403\u6392\u540d\u7b2c100\u7684\u8d85\u7ea7\u8ba1\u7b97\u673a\uff0c\u4f46\u5176\u4ec5\u80fd\u6a21\u62df\u5927\u8111\u6876\u72b6\u76ae\u5c42<\/span><\/span>\uff08Barrel Cortex\uff09<\/span><\/span>\u4e2d\u7684 31,000 \u4f59\u4e2a\u795e\u7ecf\u5143\uff0c\u76f8\u6bd4\u800c\u8a00\u4eba\u7c7b\u5927\u8111\u5927\u7ea6\u5305\u542b\u6bd4\u8fd9\u591a 100 \u4e07\u500d\u7684\u795e\u7ecf\u5143\uff0c\u795e\u7ecf\u79d1\u5b66\u7814\u7a76\u82e5\u8981\u8fdb\u884c\u5982\u6b64\u89c4\u6a21\u7684\u6a21\u62df\u5c06\u9700\u8981\u66f4\u9ad8\u6548\u7684\u8ba1\u7b97\u8d44\u6e90\u3002<\/span><\/span><\/section>\n

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                          \u56fe5\uff1a\u5b9e\u73b0\u793e\u533a\u51c6\u5907\u5ea6\u7684\u5173\u952e\u8003\u91cf<\/span><\/span><\/h4>\n

                          <\/span><\/span><\/section>\n
                          \u7814\u53d1\u9a71\u52a8\u7684\u500d\u589e\u5305\u62ec\u5f00\u53d1\u884c\u4e1a\u7279\u5b9a\u7684\u6982\u5ff5\u9a8c\u8bc1\uff0c\u5411\u4f20\u7edf\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001\u7cfb\u7edf\u4e4b\u5916\u7684\u7814\u53d1\u56e2\u961f\u63d0\u4f9b\u65e9\u671f\u4e14\u4fbf\u6377\u7684\u6280\u672f\u8bbf\u95ee\u6743\u9650\uff0c\u4ece\u800c\u5f62\u6210\u6280\u672f\u4f20\u64ad\u7684\u94fe\u5f0f\u6548\u5e94\u3002\u6982\u5ff5\u9a8c\u8bc1\u7684\u5f71\u54cd\u529b\u66f4\u5927\uff0c\u5f53\u6240\u9009\u7684\u7279\u5b9a\u9886\u57df\u80fd\u591f\u4ea7\u751f\u5916\u6ea2\u6548\u5e94\uff0c\u5c06\u6700\u7ec8\u4fc3\u8fdb\u4e3b\u6d41\u5e94\u7528\u573a\u666f\u7684\u91c7\u7528\u3002\u6b64\u5916\uff0c\u901a\u8fc7\u503e\u542c\u65e9\u671f\u91c7\u7528\u8005\u7684\u53cd\u9988\u5e76\u6fc0\u52b1\u4ed6\u4eec\u79ef\u6781\u53c2\u4e0e\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u653e\u5927\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u751f\u6001\u7cfb\u7edf\u7684\u63a8\u52a8\u529b\u3002<\/span><\/span><\/section>\n


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                          <\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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                          <\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n

                          \u4e00\u4e2a\u6f5c\u5728\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u786c\u4ef6<\/span><\/span>\u5e73\u53f0\u662f SpiNNaker2[99]\uff0c\u8be5\u82af\u7247\u662f\u4eba\u7c7b\u5927\u8111\u8ba1\u5212<\/span><\/span>\uff08Human Brain Project\uff09<\/span><\/span>\u7684\u4e00\u90e8\u5206\uff0c\u65e8\u5728\u652f\u6301\u795e\u7ecf\u79d1\u5b66\u63a2\u7d22\u3002\u66f4\u91cd\u8981\u7684\u662f\uff0cSpiNNaker2 \u4e0e Loihi 2 \u7cfb\u7edf\u5177\u5907\u7075\u6d3b\u7684\u6570\u503c\u52a0\u901f\u5668<\/span><\/span>\uff08Flexible Numerical Accelerators\uff09<\/span><\/span>\uff0c\u80fd\u591f\u5728\u591a\u4e2a\u5c42\u7ea7\u4e0a\u6a21\u62df\u795e\u7ecf\u884c\u4e3a\uff0c\u5305\u62ec\u7a81\u89e6<\/span><\/span>\uff08Synaptic Computation\uff09<\/span><\/span>\u3001\u6811\u7a81\u8ba1\u7b97<\/span><\/span>\uff08Dendritic Computation\uff09<\/span><\/span>[23,100]\u7b49\u7cbe\u7ec6\u5efa\u6a21\uff0c\u8109\u51b2\u795e\u7ecf\u5143<\/span><\/span>\uff08Spiking Point Neurons\uff09<\/span><\/span>\u3001\u901f\u7387\u795e\u7ecf\u5143<\/span><\/span>\uff08Rate Neurons\uff09<\/span><\/span>[23,99]\u7b49\u795e\u7ecf\u5143\u5c42\u7ea7\u5efa\u6a21\uff0c\u4ee5\u53ca\u5747\u573a\u6a21\u578b<\/span><\/span>\uff08Mean-Field Models\uff09<\/span><\/span>[29]\u3001\u8111\u7535\u4fe1\u53f7<\/span><\/span>\uff08Electroencephalogram, EEG\uff09<\/span><\/span>\u884c\u4e3a\u6a21\u62df[23,101]\u7b49\u4e2d\u5c3a\u5ea6<\/span><\/span>\uff08Mesoscopic\uff09<\/span><\/span>\u5efa\u6a21\u3002\u8fd9\u4e9b\u7cfb\u7edf\u8fd8\u53ef\u7528\u4e8e\u652f\u6301\u591a\u5c3a\u5ea6<\/span><\/span>\uff08Multiscale\uff09<\/span><\/span>\u8111\u6a21\u578b\uff0c\u4f8b\u5982\u865a\u62df\u8111[97]\uff0c\u5e76\u4e14\u5177\u5907\u63a5\u8fd1\u4eba\u7c7b\u5927\u8111\u89c4\u6a21\u7684\u5b9e\u65f6\u6a21\u62df\u80fd\u529b\u3002\u6b64\u5916\uff0c\u8d8a\u6765\u8d8a\u591a\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u82af\u7247\u6b63\u5728\u8fdb\u5165\u5e02\u573a\uff0c\u53ef\u4ee5\u652f\u6301\u795e\u7ecf\u79d1\u5b66\u5b9e\u9a8c[23,39,102]\u3002<\/span><\/span>\u7136\u800c\uff0c\u8981\u5b9e\u73b0\u5927\u89c4\u6a21\u3001\u7cbe\u7ec6\u5316\u7684\u795e\u7ecf\u79d1\u5b66\u63a2\u7d22\u4ecd\u9700\u8981\u8fdb\u4e00\u6b65\u7684\u6280\u672f\u53d1\u5c55\u548c\u4f18\u5316\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n

                          \u673a\u5668\u5b66\u4e60\u542f\u53d1 ML innovations<\/span><\/span><\/strong><\/h3>\n

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                          \u673a\u5668\u5b66\u4e60\u7684\u76ee\u6807\u662f\u5f00\u53d1\u80fd\u591f\u81ea\u4e3b\u5b66\u4e60\u7684\u7b97\u6cd5\uff0c\u800c\u4e0d\u662f\u4f9d\u8d56\u663e\u5f0f\u7f16\u7a0b<\/span><\/span>\uff08Explicit Programming\uff09<\/span><\/span>\u6765\u6267\u884c\u7279\u5b9a\u4efb\u52a1\u3002\u8fd1\u5e74\u6765\u7684\u7b97\u6cd5\u8fdb\u5c55\u4e0d\u4ec5\u63d0\u5347\u4e86\u795e\u7ecf\u7f51\u7edc\u7684\u89c4\u6a21\uff0c\u8fd8\u6df1\u5165\u63a2\u7d22\u4e86\u8fde\u63a5\u6a21\u5f0f<\/span><\/span>\uff08Connectivity\uff09<\/span><\/span>\u3001\u4fe1\u606f\u8868\u793a<\/span><\/span>\uff08Information Representation\uff09<\/span><\/span>\u3001\u5b66\u4e60\u673a\u5236<\/span><\/span>\uff08Learning Mechanisms\uff09<\/span><\/span>\u7b49\u65b9\u9762\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n
                          \u9664\u4e86\u7b97\u6cd5\u4f18\u5316\uff0c\u6df1\u5ea6\u5b66\u4e60\u7b97\u6cd5\u7684\u8ba1\u7b97\u9700\u6c42\u4e5f\u63a8\u52a8\u4e86\u8ba1\u7b97\u67b6\u6784\u7684\u521b\u65b0\u3002\u7136\u800c\uff0c\u8fd9\u4e9b\u8fdb\u6b65\u5e76\u975e\u5b8c\u5168\u72ec\u7acb\uff0c\u5176\u4e2d\u4e00\u4e2a\u663e\u8457\u73b0\u8c61\u662f\u201c\u786c\u4ef6\u5f69\u7968<\/span><\/span>\uff08Hardware Lottery\uff09<\/span><\/span>\u201d[103]\uff0c\u5373\u67d0\u4e9b\u8ba1\u7b97\u65b9\u6cd5\u7684\u6210\u529f\u5f80\u5f80\u5e76\u975e\u7531\u4e8e\u5176\u672c\u8eab\u7684\u4f18\u8d8a\u6027\uff0c\u800c\u662f\u56e0\u4e3a\u5b83\u4eec\u6070\u597d\u5339\u914d\u5f53\u524d\u7684\u786c\u4ef6\u67b6\u6784\u3002\u8fd9\u4e00\u73b0\u8c61\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u963b\u788d\u4e86\u795e\u7ecf\u542f\u53d1\u5f0f<\/span><\/span>\uff08Neuro-Inspired\uff09<\/span><\/span>\u673a\u5668\u5b66\u4e60\u7684\u521b\u65b0\uff0c\u56e0\u4e3a\u7f3a\u4e4f\u76f8\u5e94\u7684\u786c\u4ef6\u652f\u6301\u5bfc\u81f4\u67d0\u4e9b\u521b\u65b0\u7684\u7b97\u6cd5\u65e0\u6cd5\u9ad8\u6548\u8fd0\u884c\u3002\u56e0\u6b64\uff0c\u6211\u4eec\u8ba4\u4e3a\u5927\u89c4\u6a21\u9ad8\u6548\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u80fd\u591f\u63a8\u52a8\u795e\u7ecf\u542f\u53d1\u5f0f\u673a\u5668\u5b66\u4e60<\/span><\/span>\uff08Neuro-Inspired ML\uff09<\/span><\/span>[104,105]\u3002\u56fe3\u4e2d\u6240\u8bc6<\/span><\/span>\u522b\u7684\u5173\u952e\u795e\u7ecf\u5f62\u6001\u7279\u6027\uff0c\u53ef\u4f5c\u4e3a\u672a\u6765\u673a\u5668\u5b66\u4e60\u521b\u65b0\u7684\u84dd\u56fe\u3002<\/span><\/span><\/section>\n

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                          \u6700\u91cd\u8981\u7684\u662f\uff0c\u8fd9\u4e9b\u6280\u672f\u8fdb\u6b65\u7684\u878d\u5408\u6807\u5fd7\u7740\u8ba1\u7b97\u8303\u5f0f\u7684\u8f6c\u53d8\uff1a\u4f20\u7edf\u51af\u00b7\u8bfa\u4f9d\u66fc<\/span><\/span>\uff08von Neumann\uff09<\/span><\/span>\u8ba1\u7b97\u67b6\u6784\u901a\u5e38\u5c06\u7b97\u6cd5\u548c\u786c\u4ef6\u4f5c\u4e3a\u76f8\u4e92\u72ec\u7acb\u7684\u90e8\u5206\uff0c\u800c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5219\u5f3a\u8c03\u7b97\u6cd5\u4e0e\u67b6\u6784\u7684\u4e92\u8054\u5219\u66f4\u7c7b\u4f3c\u4e8e\u5927\u8111\u7684\u8bbe\u8ba1\u65b9\u5f0f\uff0c\u4f7f\u5f97\u795e\u7ecf\u5f62\u6001\u786c\u4ef6\u7684\u53d1\u5c55\u53ef\u80fd\u4f1a\u5728\u795e\u7ecf\u7f51\u7edc\u673a\u5668\u5b66\u4e60\u9886\u57df\u5e26\u6765\u524d\u6240\u672a\u6709\u7684\u53d8\u9769\u3002\u5927\u8111\u7684\u8ba1\u7b97\u6a21\u5f0f\u5929\u7136\u4f53\u73b0\u4e86\u5b66\u4e60\u7b97\u6cd5\u7684\u672c\u8d28\u2014\u2014\u795e\u7ecf\u5143\u53c2\u6570\u4f1a\u968f\u5b66\u4e60\u8fc7\u7a0b\u800c\u81ea\u9002\u5e94\u5730\u8c03\u6574\u529f\u80fd\uff0c\u800c\u4f20\u7edf\u786c\u4ef6<\/span><\/span>\uff08\u5982\u6570\u636e\u4e2d\u5fc3\u7684GPU\uff09<\/span><\/span>\u91c7\u7528\u7684\u662f\u56fa\u5b9a\u7684\u8bad\u7ec3\u6a21\u5f0f\uff0c\u4e0e\u5927\u8111\u7684\u5b66\u4e60\u673a\u5236\u5b58\u5728\u663e\u8457\u533a\u522b\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7ed3\u5408\u65b0\u5174\u5668\u4ef6\u6280\u672f\u80fd\u591f\u63d0\u4f9b\u4e00\u79cd\u514b\u670d\u5f53\u524d\u673a\u5668\u5b66\u4e60\u8ba1\u7b97\u4e0e\u5b58\u50a8\u5206\u79bb\u5173\u952e\u74f6\u9888\u7684\u72ec\u7279\u89e3\u51b3\u65b9\u6848\uff0c\u4ece\u800c\u63a8\u52a8\u4eba\u5de5\u667a\u80fd\u5411\u66f4\u9ad8\u6548\u3001\u66f4\u63a5\u8fd1\u751f\u7269\u5b66\u4e60\u6a21\u5f0f\u7684\u65b9\u5411\u53d1\u5c55\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n

                          \u65b0\u5174\u5668\u4ef6\u4e0e\u8ba1\u7b97\u67b6\u6784 Emerging devices and architectures<\/span><\/span><\/strong><\/h3>\n

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                          \u9ad8\u6548\u7684\u5b58\u50a8\u6280\u672f\u5bf9\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u53ef\u6269\u5c55\u6027\u81f3\u5173\u91cd\u8981\u3002\u4ece\u4fe1\u606f\u5b58\u50a8<\/span><\/span>\uff08Storage\uff09<\/span><\/span>\u3001\u5de9\u56fa<\/span><\/span>\uff08Consolidation\uff09<\/span><\/span>\u5230\u68c0\u7d22<\/span><\/span>\uff08Retrieval\uff09<\/span><\/span>\uff0c\u5b58\u50a8\u5668\u6784\u6210\u4e86\u5b66\u4e60\u548c\u57fa\u4e8e\u7ecf\u9a8c\u89e3\u51b3\u95ee\u9898\u7684\u57fa\u7840\u3002\u65b0\u5174\u5fc6\u963b\u5668<\/span><\/span>\uff08Memristive Devices\uff09<\/span><\/span>\u5728\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u9886\u57df\u5c24\u5176\u5c55\u73b0\u51fa\u5de8\u5927\u6f5c\u529b\uff0c\u5176\u975e\u6613\u5931\u6027<\/span><\/span>\uff08Non-Volatility\uff09<\/span><\/span>\u3001\u9ad8\u5b58\u50a8\u5bc6\u5ea6<\/span><\/span>\uff08High Density\uff09<\/span><\/span>\u548c\u591a\u72b6\u6001\u5b58\u50a8\u7279\u6027<\/span><\/span>\uff08Multistate Properties<\/span><\/span>\uff09\uff08\u8be6\u89c1\u68463\uff09<\/span><\/span>\uff0c\u4f7f\u5176\u6210\u4e3a\u6784\u5efa\u9ad8\u6548\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u5173\u952e\u6280\u672f\u4e4b\u4e00\u3002\u6b64\u5916\uff0c\u5fc6\u963b\u5668\u9635\u5217<\/span><\/span>\uff08Memristor Arrays\uff09<\/span><\/span>\u80fd\u591f\u76f4\u63a5\u6267\u884c\u4e58-\u7d2f\u52a0<\/span><\/span>\uff08Multiply and Accumulate, MAC\uff09<\/span><\/span>\u8fd0\u7b97\uff0c\u800c\u8fd9\u6b63\u662f\u795e\u7ecf\u7f51\u7edc\u7684\u6838\u5fc3\u8ba1\u7b97\u64cd\u4f5c\uff0c\u5176\u57fa\u4e8e\u6b27\u59c6\u5b9a\u5f8b<\/span><\/span>\uff08Ohm\u2019s Law\uff09<\/span><\/span>\u5b9e\u73b0\u7d27\u51d1\u4e14\u4f4e\u529f\u8017\u7684\u8ba1\u7b97\u52a0<\/span><\/span>\u901f[106,107]\u3002\u8981\u4f7f\u65b0\u5174\u5b58\u50a8\u6280\u672f\u5177\u5907\u5b9e\u9645\u53ef\u884c\u6027\uff0c\u7406\u60f3\u60c5\u51b5\u4e0b\u5e94\u6ee1\u8db3\u4ee5\u4e0b\u7279\u6027\uff1a\u4f4e\u80fd\u8017\uff1a\u2248fJ\/bit<\/span><\/span>\uff08\u98de\u7126\u8033\u6bcf\u6bd4\u7279\uff09<\/span><\/span>\uff0c\u4f4e\u5ef6\u8fdf\uff0c\u4f4e\u5de5\u4f5c\u7535\u538b<\/span><\/span>\uff08Low Operating Voltage\uff09<\/span><\/span>\uff1a<1V\uff0c\u9ad8\u8010\u4e45\u5ea6\uff1a>10\u00b9\u2077\u6b21\u5faa\u73af\uff0c\u9ad8\u6570\u636e\u4fdd\u6301\u80fd\u529b<\/span><\/span>\uff08High Data Retention\uff09<\/span><\/span>\uff0c\u9ad8\u53ef\u6269\u5c55\u6027\uff1a<10nm\uff0c\u517c\u5bb9CMOS\u5de5\u827a[108]\u7b49\u3002<\/span><\/span><\/section>\n


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                          <\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/h3>\n


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                          \u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u5728\u591a\u4e2a\u9886\u57df\u5177\u6709\u5de8\u5927\u7684\u6f5c\u5728\u5f71\u54cd\uff0c\u5f53\u524d\u6b63\u5904\u4e8e\u5927\u89c4\u6a21\u521b\u65b0\u7684\u5173\u952e\u65f6\u523b\u3002\u8be5\u9886\u57df\u5df2\u4ece\u5b66\u672f\u673a\u6784\u5f00\u53d1\u7684\u539f\u578b\u7cfb\u7edf\uff0c\u53d1\u5c55\u5230\u5177\u5907\u4e8b\u4ef6\u9a71\u52a8\u5904\u7406\u3001\u5b66\u4e60\u6a21\u578b\u548c\u8bbe\u8ba1\u5de5\u5177\uff0c\u5e76\u4e0e\u73b0\u5b9e\u4e16\u754c\u5b9e\u9a8c\u76f8\u7ed3\u5408\u7684\u751f\u4ea7\u7ea7\u7cfb\u7edf\u3002\u5728\u6b64\u57fa\u7840\u4e0a\uff0c\u5de5\u4e1a\u754c\u5df2\u63a8\u52a8\u66f4\u5148\u8fdb\u7684\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\uff0c\u5e76\u6269\u5c55\u5176\u5e94\u7528\u81f3\u591a\u4e2a\u9886\u57df\uff0c\u5305\u62ec\u79d1\u5b66\u8ba1\u7b97<\/span><\/span>\uff08Scientific Computing\uff09<\/span><\/span>\u3001\u589e\u5f3a\/\u865a\u62df\u73b0\u5b9e<\/span><\/span>\uff08AR\/VR, Augmented\/Virtual Reality\uff09<\/span><\/span>\u3001\u53ef\u7a7f\u6234\u8bbe\u5907<\/span><\/span>\uff08Wearables\uff09<\/span><\/span>\u3001\u667a\u6167\u519c\u4e1a<\/span><\/span>\uff08Smart Farming\uff09<\/span><\/span>\u3001\u667a\u6167\u57ce\u5e02<\/span><\/span>\uff08Smart Cities\uff09<\/span><\/span>\u7b49\u3002\u8981\u7ee7\u7eed\u63a8\u52a8\u8be5\u9886\u57df\u7684\u53d1\u5c55\uff0c\u9700\u8981\u5927\u6279\u5de5\u7a0b\u5e08\u548c\u79d1\u5b66\u5bb6\u4ee5\u53ca\u516c\u5171\u548c\u79c1\u8425\u673a\u6784\u5728\u5171\u4eab\u76ee\u6807\u4e0b\u5f00\u5c55\u5408\u4f5c\u3002\u672c\u7814\u7a76\u63d0\u51fa\u4e86\u4e00\u4efd\u53d1\u5c55\u8def\u7ebf\u56fe\uff0c\u603b\u7ed3\u4e86\u8be5\u9886\u57df\u7684\u5173\u952e\u5f00\u653e\u95ee\u9898\u4ee5\u6fc0\u53d1\u672a\u6765\u7684\u7814\u7a76\u65b9\u5411\u3002<\/span><\/span><\/section>\n

                          <\/span><\/span><\/section>\n

                          \u9996\u5148\uff0c\u6211\u4eec\u5e94\u5168\u9762\u5ba1\u89c6\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u6240\u80fd\u652f\u6301\u7684\u80fd\u529b\uff0c\u5305\u62ec\u8bc6\u522b\u901a\u7528\u8ba1\u7b97\u5355\u5143<\/span><\/span>\uff08Common Primitives\uff09<\/span><\/span>\uff0c\u5e76\u4ee5\u5f02\u6784\u65b9\u5f0f\u8fdb\u884c\u8fde\u63a5\u6765\u6574\u5408\u4e0d\u540c\u7ec4\u4ef6\uff0c\u5f00\u53d1\u591a\u7528\u9014\u67b6\u6784\u3002\u4eba\u7c7b\u5927\u8111\u7531\u591a\u4e2a\u4e0d\u540c\u4f46\u9ad8\u5ea6\u4e13\u4e1a\u5316\u7684\u533a\u57df\u7ec4\u6210\uff0c\u5e76\u4ee5\u5206\u5e03\u5f0f\u65b9\u5f0f\u8fde\u63a5\uff0c\u4f46\u76ee\u524d\u8fd9\u4e9b\u8111\u533a\u6a21\u5757\u4e0e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u9886\u57df\u4f7f\u7528\u7684\u667a\u80fd\u7ec4\u4ef6<\/span><\/span>\uff08\u5728\u66f4\u7cbe\u7ec6\u7684\u5c42\u9762\u4e0a\uff09<\/span><\/span>\u5e76\u6ca1\u6709\u4e00\u4e00\u5bf9\u5e94\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u7ec4\u6307\u5bfc\u6027\u7279\u5f81\u7528\u4e8e\u8bc6\u522b\u8ba1\u7b97\u5355\u5143\uff0c\u4ee5\u6784\u5efa\u66f4\u5f3a\u5927\u3001\u9002\u5e94\u6027\u66f4\u5f3a\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u3002\u8fd9\u4e9b\u7cfb\u7edf\u5e94\u5f53\u652f\u6301\u5e7f\u6cdb\u7684\u5b66\u4e60\u673a\u5236\uff0c\u4f8b\u5982\u5728\u7ebf\u6301\u7eed\u5b66\u4e60\u3001\u57fa\u4e8e\u4e8b\u4ef6\u9a71\u52a8\u4f20\u611f\u5668\u6570\u636e\u7684\u5b9e\u65f6\u51b3\u7b56\u3001\u4f20\u611f-\u8fd0\u52a8\u878d\u5408\u7b97\u6cd5<\/span><\/span>\uff08Sensorimotor Fusion Algorithms\uff09<\/span><\/span>\u3001\u591a\u6a21\u6001\u5b66\u4e60<\/span><\/span>\uff08Multimodal Learning\uff09<\/span><\/span>\u3001\u9884\u6d4b\u5efa\u6a21<\/span><\/span>\uff08Predictive Modelling\uff09<\/span><\/span>\u7b49\u3002\u76ee\u524d\u6211\u4eec\u4e0d\u5e94\u5c40\u9650\u4e8e\u5355\u4e00\u786c\u4ef6\u5e73\u53f0\u6216\u90e8\u5206\u7279\u6027\uff0c\u800c\u662f\u5e94\u63a2\u7d22\u591a\u6837\u5316\u7684\u80fd\u529b\uff0c\u5e76\u660e\u786e\u4e0d\u540c\u5e94\u7528\u573a\u666f\u4e0b\u54ea\u4e9b\u795e\u7ecf\u5f62\u6001\u7279\u6027\u81f3\u5173\u91cd\u8981\u3002\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u4e0d\u5e94\u91c7\u7528\u201d\u4e00\u5200\u5207\u201d\u7684\u89e3\u51b3\u65b9\u6848\uff0c\u76f8\u53cd\uff0c\u4e0d\u540c\u5e94\u7528\u573a\u666f\u53ef\u80fd\u9700\u8981\u5177\u6709\u4e0d\u540c\u7279\u6027\u7684\u795e\u7ecf\u5f62\u6001\u786c\u4ef6\u89e3\u51b3\u65b9\u6848\u3002\u5f53\u524d\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u4e3b\u8981\u57fa\u4e8e\u5355\u82af\u7247\u6216\u5355<\/span><\/span>\u677f\uff0c\u5728\u5927\u89c4\u6a21\u7cfb\u7edf\u4e2d\u53ef\u80fd\u5e76\u4e0d\u9002\u7528\u3002\u4ece\u8fd9\u4e00\u95ee\u9898\u51fa\u53d1\uff0c\u6211\u4eec\u9700\u8981\u7814\u7a76\u4e24\u4e2a\u65b9\u5411\uff1a<\/span><\/span><\/p>\n

                            \n
                          1. \n

                            \u6784\u5efa\u5206\u5e03\u5f0f\u3001\u5f02\u6b65\u795e\u7ecf\u7f51\u7edc\u7684\u9c81\u68d2\u6027\u6a21\u578b\u548c\u7b97\u6cd5<\/span><\/span>\uff08Robust Models and Algorithms for Distributed and Asynchronous Systems\uff09<\/span><\/span>\uff0c\u8fd9\u9700\u8981\u66f4\u5927\u89c4\u6a21\u7684\u534f\u4f5c\u52aa\u529b\uff0c\u5e76\u975e\u7b80\u5355\u6311\u6218\u3002<\/span><\/span><\/p>\n<\/li>\n

                          2. \n
                            \u6269\u5c55\u5b66\u4e60\u6a21\u578b\u81f3\u66f4\u5927\u89c4\u6a21\u7684\u7f51\u7edc\u548c\u8ba1\u7b97\u673a<\/span><\/span>\uff08Scaling Up Learning Models to Larger Networks and Machines\uff09<\/span><\/span>\uff0c\u53ef\u4ee5\u501f\u9274AI \u793e\u533a\u7684\u505a\u6cd5\uff0c\u5c06\u5927\u89c4\u6a21\u6a21\u578b\u90e8\u7f72\u5728\u4e91\u7aef<\/span><\/span>\uff08Cloud\uff09<\/span><\/span>\u6216\u5f02\u6784\u8ba1\u7b97\u7cfb\u7edf<\/span><\/span>\uff08Heterogeneous Systems\uff09<\/span><\/span>\u4e0a\u3002\u5982\u679c\u8fd9\u4e00\u76ee\u6807\u80fd\u591f\u5b9e\u73b0\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u7684\u91c7\u7528\u7387\u4e5f\u5c06\u81ea\u7136\u63d0\u5347\u3002<\/span><\/span><\/section>\n<\/li>\n<\/ol>\n

                            <\/span><\/span><\/section>\n
                            \u5f53\u524d\uff0c\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u8fdb\u5c55\u5f80\u5f80\u53d7\u9650\u4e8e\u5b9e\u9a8c\u5ba4\u4e2d\u7684\u5c0f\u89c4\u6a21\u539f\u578b\u7cfb\u7edf\u548c\u7814\u7a76\u8d44\u6e90\u7684\u5c01\u95ed\u6027\uff0c\u5bfc\u81f4\u5916\u90e8\u4e13\u5bb6\u548c\u975e\u4e13\u5bb6\u96be\u4ee5\u8bbf\u95ee\u8fd9\u4e9b\u6d4b\u8bd5\u5e73\u53f0\u3002\u8fd9\u4f7f\u5f97\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5728\u53ef\u79fb\u690d\u6027<\/span><\/span>\uff08Portability\uff09<\/span><\/span>\u3001\u6807\u51c6\u5316\u548c\u8de8\u5b66\u79d1\u77e5\u8bc6\u6d41\u901a\u65b9\u9762\u9047\u5230\u6311\u6218\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\uff0c\u6211\u4eec\u53ef\u4ee5\u63a8\u52a8\u5f00\u653e\u6d4b\u8bd5\u5e73\u53f0\u548c\u57fa\u51c6\u6d4b\u8bd5\u5e93\u7684\u5efa\u8bbe\uff0c\u6765\u5f00\u53d1\u65b0\u4e00\u4ee3\u7684\u5927\u89c4\u6a21\u3001\u81ea\u9002\u5e94\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u3002\u5f53\u539f\u578b\u6d4b\u8bd5\u5e73\u53f0\u80fd\u591f\u4e0e\u4f20\u7edf\u8ba1\u7b97\u7cfb\u7edf\u96c6\u6210\u65f6\uff0c\u5c06\u4fc3\u8fdb\u7814\u53d1\u7684\u5feb\u901f\u589e\u957f\u5e76\u52a0\u901f\u9762\u5411\u5e94\u7528\u7684\u7814\u7a76\u3002\u6b64\u5916\uff0c\u8fd9\u4e9b\u5f00\u653e\u8d44\u6e90\u9700\u8981\u5177\u5907\u53ef\u6d4b\u91cf\u7684\u6027\u80fd\u6307\u6807\u6216\u5f00\u653e\u57fa\u51c6\u6d4b<\/span><\/span>\u8bd5[94]\u3002<\/span><\/span>\u6211\u4eec\u8fd8\u53ef\u4ee5\u501f\u9274\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u6210\u529f\u7684\u5b66\u672f-\u5de5\u4e1a\u5408\u4f5c\uff0c\u8fdb\u4e00\u6b65\u52a0\u901f\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u7814\u53d1\u5468\u671f\u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u8fd9\u4e5f\u80fd\u4e3a\u6df1\u5ea6\u5b66\u4e60\u793e\u533a\u5e26\u6765\u65b0\u7684\u601d\u8def\uff0c\u63a2\u7d22\u66f4\u7c7b\u4f3c\u4eba\u7c7b\u5b66\u4e60\u65b9\u5f0f\u7684\u8ba1\u7b97\u65b9\u6cd5\u3002<\/span><\/span><\/section>\n

                            <\/span><\/span><\/section>\n
                            \u8fd1\u5e74\u6765\uff0c\u7528\u4e8e\u5feb\u901f\u539f\u578b\u5f00\u53d1\u6216\u8bc4\u4f30\u7684\u8f6f\u4ef6\u548c\u786c\u4ef6\u8bbe\u8ba1\u5de5\u5177\u5448\u589e\u957f\u8d8b\u52bf\uff0c\u7136\u800c\u76ee\u524d\u4ecd\u7136\u7f3a\u4e4f\u4e00\u4e2a\u5b8c\u6574\u7684\u8f6f\u4ef6\u751f\u6001\u7cfb\u7edf\u548c\u793e\u533a\u8bba\u575b\uff0c\u6765\u540c\u65f6\u652f\u6301\u4e13\u5bb6\u548c\u975e\u4e13\u5bb6\u7684\u9700\u6c42\u3002\u867d\u7136\u90e8\u5206\u884c\u4e1a\u5408\u4f5c\u4f19\u4f34\u5df2\u7ecf\u5f00\u59cb<\/span><\/span>\u586b\u8865\u8fd9\u4e00\u7a7a\u767d\uff0c\u4f46\u5927\u90e8\u5206\u5de5\u5177\u7684\u5f00\u53d1\u4ecd\u7136\u662f\u5b64\u7acb\u7684\uff0c\u7f3a\u4e4f\u7cfb\u7edf\u6027\u7684\u6574\u5408\u4e0e\u6807\u51c6\u5316<\/span><\/span>\uff08\u89c1\u56fe4\uff09<\/span><\/span>\u3002<\/span><\/span>\u672a\u6765\uff0c\u6211\u4eec\u9700\u8981\u63a8\u52a8\u5de5\u5177\u94fe\u7684\u53d1\u5c55\uff0c\u4f7f\u5176\u80fd\u591f\u4e0e\u4e3b\u6d41\u6df1\u5ea6\u5b66\u4e60\u5de5\u5177\u517c\u5bb9\uff0c\u6216\u76f4\u63a5\u53d7\u76ca\u4e8e\u6df1\u5ea6\u5b66\u4e60\u5de5\u5177\u7684\u6210\u719f\u751f\u6001\u7cfb\u7edf\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u5e94\u5173\u6ce8\u5927\u89c4\u6a21\u4e8b\u4ef6\u9a71\u52a8\u7cfb\u7edf\u7684\u5173\u952e\u5e94\u7528\uff0c\u7279\u522b\u662f\u5728\u975e\u8ba4\u77e5\u8ba1\u7b97<\/span><\/span>\uff08Non-Cognitive Applications\uff09<\/span><\/span>\u3001\u8fb9\u7f18\u7aef\u5206\u5e03\u5f0f\u667a\u80fd\u4ee5\u53ca\u795e\u7ecf\u5f62\u6001\u7cfb\u7edf\u4e0e\u6df1\u5ea6\u5b66\u4e60\u7cfb\u7edf\u96c6\u6210\u7684\u7279\u6b8a\u573a\u666f\u3002<\/span><\/span><\/section>\n

                            <\/span><\/span><\/section>\n
                            \u6700\u540e\uff0c\u6211\u4eec\u63d0\u51fa\u672a\u6765\u53ef\u7814\u7a76\u7684\u5f00\u653e\u95ee\u9898\uff0c\u8fd9\u4e9b\u95ee\u9898\u53ef\u5206\u4e3a\u77ed\u671f\u5185\u53ef\u7814\u7a76\u7684\u95ee\u9898<\/span><\/span>\uff08\u68464\uff09<\/span><\/span>\u3001\u4e2d\u671f\u63a2\u7d22\u7684\u95ee\u9898<\/span><\/span>\uff08\u68465\uff09<\/span><\/span>\u4ee5\u53ca\u957f\u671f\u5f85\u89e3\u7684\u95ee\u9898<\/span><\/span>\uff08\u68466\uff09<\/span><\/span>\u3002<\/span><\/span>\u5f53\u524d\u6b63\u662f\u6295\u8eab\u4e8e\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5927\u89c4\u6a21\u53d1\u5c55\u7684\u7406\u60f3\u65f6\u673a\uff0c\u8fd9\u4e00\u9886\u57df\u7684\u53d1\u5c55\u5c06\u4e3a\u672a\u6765\u7684\u4eba\u5de5\u667a\u80fd\u7cfb\u7edf\u548c\u81ea\u7136\u667a\u80fd\u7cfb\u7edf\u5e26\u6765\u7a81\u7834\u6027\u8fdb\u5c55\u3002<\/span><\/span><\/section>\n

                            <\/span><\/span><\/section>\n
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                            <\/span><\/section>\n<\/section>\n
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                            \u68464\uff1a\u77ed\u671f\u7814\u7a76\u95ee\u9898<\/span><\/span><\/p>\n


                            <\/span><\/span><\/p>\n

                            \u786c\u4ef6\/\u8f6f\u4ef6\u534f\u540c\u8bbe\u8ba1\u6846\u67b6\uff1a\u6211\u4eec\u662f\u5426\u80fd\u591f\u8bbe\u8ba1\u7528\u6237\u53cb\u597d\u7684\u786c\u4ef6\/\u8f6f\u4ef6\u534f\u540c\u8bbe\u8ba1\u6846\u67b6\uff0c\u4f7f\u975e\u4e13\u5bb6\u4e5f\u80fd<\/span><\/span>\u5f00\u53d1\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u6a21\u578b\uff1f\u6b64\u5916\uff0c\u8fd9\u79cd\u6846\u67b6\u662f\u5426\u53ef\u4ee5\u5728\u5927\u89c4\u6a21\u7cfb\u7edf\u4e2d\u9ad8\u6548\u5e94\u7528\uff1f<\/span><\/span><\/p>\n


                            <\/span><\/span><\/p>\n

                            \u539f\u578b\u7cfb\u7edf\uff1a\u9762\u5411\u5927\u89c4\u6a21\u7cfb\u7edf\u7684\u539f\u578b\u6d4b\u8bd5\u5e73\u53f0\u5e94\u5177\u5907\u54ea\u4e9b\u7279\u6027\uff1f\u5982\u4f55\u5411\u66f4\u5e7f\u6cdb\u7684\u7814\u7a76\u793e\u533a\u63d0\u4f9b\u5bf9\u8fd9\u4e9b\u539f\u578b\u7684\u8bbf\u95ee\u6743\u9650\uff1f<\/span><\/span><\/p>\n


                            <\/span><\/span><\/p>\n

                            \u7cfb\u7edf\u96c6\u6210\uff1a\u5982\u4f55\u63d0\u5347\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u4e0e\u4f20\u7edf\u8ba1\u7b97\u7cfb\u7edf\uff08\u5982\u52a0\u901f\u5668\uff09\u7684\u96c6\u6210\u4fbf\u6377\u6027\uff1f<\/span><\/span><\/p>\n


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                            \u8f6f\u4ef6\u5de5\u5177\uff1a\u5982\u4f55\u8bbe\u8ba1\u5f00\u653e\u6e90\u7801\u7684\u8f6f\u4ef6\u5de5\u5177\uff0c\u4f7f\u5176\u80fd\u591f\u4e0e\u4e3b\u6d41\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u4e92\u64cd\u4f5c\uff1f\u662f\u5426\u53ef\u4ee5\u5f00\u53d1\u66f4\u9ad8\u62bd\u8c61\u5c42\u6b21\u7684\u8f6f\u4ef6\u5de5\u5177\uff0c\u4f7f\u5176\u57fa\u4e8e\u529f\u80fd\u6a21\u5757\u5e93\uff0c\u5e76\u5177\u5907\u901a\u7528\u4e2d\u95f4\u8868\u793a\u5c42\uff1f<\/span><\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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                            \u68465\uff1a\u4e2d\u671f\u7814\u7a76\u95ee\u9898\uff08Medium-Term Questions\uff09<\/span><\/span><\/p>\n


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                            \u5927\u89c4\u6a21\u6d4b\u8bd5\u5e73\u53f0\uff1a\u5982\u4f55\u5728\u901a\u7528\u6d4b\u8bd5\u5e73\u53f0\u4e0a\u90e8\u7f72\u5f02\u6784\u7684\u3001\u5927\u89c4\u6a21\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u6f14\u793a\uff1f<\/span><\/span><\/p>\n


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                            \u57fa\u51c6\u6d4b\u8bd5\uff1a\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u901a\u7528\u57fa\u51c6\u6d4b\u8bd5\u5957\u4ef6\u5e94\u5305\u62ec\u54ea\u4e9b\u5185\u5bb9\uff1f\u5982\u4f55\u5bf9\u8fd9\u4e9b\u57fa\u51c6\u6d4b\u8bd5\u8fdb\u884c\u6709\u6548\u8bc4\u4f30\uff1f\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7684\u5f00\u6e90\u534f\u8bae\u5e94\u5177\u5907\u54ea\u4e9b\u7279\u6027\uff1f<\/span><\/span><\/p>\n


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                            \u7ec8\u751f\u5b66\u4e60\uff1a\u4e8b\u4ef6\u9a71\u52a8\u7684\u5c40\u90e8\u5b66\u4e60\u548c\u53ef\u5851\u6027\u5982\u4f55\u652f\u6301\u5177\u5907\u7ec8\u751f\u5b66\u4e60\u80fd\u529b\u7684\u667a\u80fd\u673a\u5668\uff1f\u662f\u5426\u5b58\u5728\u9002\u7528\u4e8e\u8fd9\u4e9b\u7cfb\u7edf\u7684\u7edf\u4e00\u67b6\u6784\uff1f<\/span><\/span><\/p>\n


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                            \u52a8\u6001\u5efa\u6a21\uff1a\u5982\u4f55\u5728\u672a\u6765\u7684\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u7cfb\u7edf\u4e2d\u5f15\u5165\u590d\u6742\u52a8\u529b\u5b66\uff1f\u6211\u4eec\u9700\u8981\u63a2\u7d22\u5177\u5907\u66f4\u590d\u6742\u52a8\u529b\u5b66\u7684\u795e\u7ecf\u5f62\u6001\u5668\u4ef6\uff0c\u5982\u7a81\u89e6\u5b66\u4e60\u3001\u6811\u7a81\u5904\u7406\uff08Dendritic Processing\uff09\u3001\u66f4\u7cbe\u7ec6\u7684\u795e\u7ecf\u8fde\u63a5\u5efa\u6a21\u3002<\/span><\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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                            \u5173\u952e\u5e94\u7528\uff1a\u5982\u4f55\u5c06\u795e\u7ecf\u5f62\u6001\u8ba1\u7b97\u5e94\u7528\u4e8e\u4efb\u52a1\u5173\u952e\u578b\uff08Mission-Critical\uff09\u6216\u5b89\u5168\u5173\u952e\u578b\uff08Safety-Critical\uff09\u7684\u573a\u666f\uff1f<\/span><\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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                            [1] Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous systems. https:\/\/www.tensorflow.org\/ (2015).<\/span><\/span><\/section>\n
                            [2] Abreu, S. et al. Neuromorphic intermediate representation. Zenodo https:\/\/doi.org\/10.5281\/zenodo.8105042 (2023).<\/span><\/span><\/section>\n
                            [3] Aimone, J. B., Severa, W. & Vineyard, C. M. in Proc. International Conference on Neuromorphic Systems, 1\u20138 (ACM, 2019).<\/span><\/span><\/section>\n
                            [4] Amir, A. et al. in Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition 7243\u20137252 (IEEE, 2017).<\/span><\/span><\/section>\n
                            [5] Arthur, J. V. & Boahen, K. Learning in silicon: timing is everything. Adv. Neural Inf. Process. Syst.18 (2005).<\/span><\/span><\/section>\n
                            [6] Baby, S. A., Vinod, B., Chinni, C. & Mitra, K. in 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) 316\u2013321 (IEEE, 2017).<\/span><\/span><\/section>\n
                            [7] Barnell, M., Raymond, C., Brown, D., Wilson, M. & Cote, E. in Proc. 2019 IEEE High Performance Extreme Computing Conference (HPEC) 1\u20135 (IEEE, 2019).<\/span><\/span><\/section>\n
                            [8] Bergstra, J. et al. in Proc. 9th Python in Science Conference (eds van der Walt, S. & Millman, J.) 18\u201324 (2010).<\/span><\/span><\/section>\n
                            [9] Cardwell, S. G. Achieving extreme heterogeneity: codesign using neuromorphic processors. Technical Report, Sandia National Laboratories (2021). A discussion on the need for innovative co-design tools and architectures to integrate neuromorphic computing, inspired by properties of the brain, with conventional computing platforms to enhance high-performance-computing capabilities.<\/span><\/span><\/section>\n
                            [10] Cardwell, S. G. et al. in Proc. 2022 IEEE International Conference on Rebooting Computing (ICRC) 57\u201365 (IEEE, 2022).<\/span><\/span><\/section>\n
                            [11] Collobert, R., Bengio, S. & Mari\u00e9thoz, J. Torch: a modular machine learning software library. Technical Report (IDIAP, 2002).<\/span><\/span><\/section>\n
                            [12] Delbruck, T. jAER open source project. https:\/\/jaerproject.org (2007).<\/span><\/span><\/section>\n
                            [13] Demira\u011f, Y. et al. in Proc. 2021 IEEE International Symposium on Circuits and Systems (ISCAS) 1\u20135 (IEEE, 2021).<\/span><\/span><\/section>\n
                            [14] Fatemi, H., Karia, V., Pandit, T. & Kudithipudi, D. in Proc. Research Symposium on Tiny Machine Learning 1\u20138 (2021).<\/span><\/span><\/section>\n
                            [15] Finateu, T. et al. in Proc. 2020 IEEE International Solid-State Circuits Conference – (ISSCC) 112\u2013114 (IEEE, 2020).<\/span><\/span><\/section>\n
                            [16] Furber, S. & Bogdan, P. (eds) SpiNNaker: A Spiking Neural Network Architecture (now publishers, 2020). A book that explores the development of SpiNNaker-1, a large-scale neuromorphic computing (1 million core) processor platform optimized for simulating spiking neural networks, which will make use of advanced technology features to achieve cutting-edge power consumption and scalability.<\/span><\/span><\/section>\n
                            [17] Gonzalez, H. A. et al. SpiNNaker2: a large-scale neuromorphic system for event-based and asynchronous machine learning. Machine Learning with New Compute Paradigms Workshop at NeurIPS (MLNPCP) (2023).<\/span><\/span><\/section>\n
                            [18] Holler, Tam, Castro & Benson. in Proc. International 1989 Joint Conference on Neural Networks 191\u2013196 (IEEE, 1989).<\/span><\/span><\/section>\n
                            [19] H\u00f6ppner, S. et al. The SpiNNaker 2 processing element architecture for hybrid digital neuromorphic computing. Preprint at https:\/\/arxiv.org\/abs\/2103.08392 (2022).<\/span><\/span><\/section>\n
                            [20] Intel. Lava Software Framework. https:\/\/lava-nc.org\/ (2021).<\/span><\/span><\/section>\n
                            [21] Jajal, P. et al. Interoperability in Deep Learning: A User Survey and Failure Analysis of ONNX Model Converters. In Proc. of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) (ACM, 2024).<\/span><\/span><\/section>\n
                            [22] Jordan, J. et al. NEST 2.18. 0. Technical Report, J\u00fclich Supercomputing Center (2019).<\/span><\/span><\/section>\n
                            [23] Jouppi, N. P. et al. Tpu v4: in Proc. 50th Annual International Symposium on Computer Architecture 1\u201314 (ACM, 2023).<\/span><\/span><\/section>\n
                            [24] Karia, V., Zohora, F. T., Soures, N. & Kudithipudi, D. in Proc. 2022 IEEE International Symposium on Circuits and Systems (ISCAS) 1372\u20131376 (IEEE, 2022).<\/span><\/span><\/section>\n
                            [25] Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst.25 (2012). A turning point in artificial intelligence research. The introduction of AlexNet was important because it introduced a deep convolutional neural network trained on a massive ImageNet dataset using GPUs, making use of transfer learning and achieving human-level recognition rates with very low error rates.<\/span><\/span><\/section>\n
                            [26] Langroudi, H. F. et al. in Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition 3100\u20133109 (2021).<\/span><\/span><\/section>\n
                            [27] Lenz, G. et al. Tonic: event-based datasets and transformations. Zenodo https:\/\/doi.org\/10.5281\/zenodo.5079802 (2021). Documentation available under https:\/\/tonic.readthedocs.io.<\/span><\/span><\/section>\n
                            [28] Li, H. et al. in Proc. 2016 IEEE Symposium on VLSI Technology 1\u20132 (IEEE, 2016).<\/span><\/span><\/section>\n
                            [29] Li, S. et al. in Proc. 2016 IEEE International Symposium on Circuits and Systems (ISCAS) 125\u2013128 (IEEE, 2016).<\/span><\/span><\/section>\n
                            [30] Liu, X. et al. in Proc. 52nd Annual Design Automation Conference 1\u20136 (ACM, 2015).<\/span><\/span><\/section>\n
                            [31] Liu, Y., Yanguas-Gil, A., Madireddy, S. & Li, Y. in Proc. 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE) 1\u20136 (IEEE, 2023).<\/span><\/span><\/section>\n
                            [32] Magma \u2014 Lava documentation. https:\/\/lava-nc.org\/lava\/lava.magma.html (2021).<\/span><\/span><\/section>\n
                            [33] Manna, D. L., Vicente-Sola, A., Kirkland, P., Bihl, T. J. & Di Caterina, G. in Proc. Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science (eds Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C. & Pimenidis, E.) 227\u2013238 (Springer, 2023).<\/span><\/span><\/section>\n
                            [34] The NEURON simulator \u2014 NEURON documentation. https:\/\/nrn.readthedocs.io\/en\/8.2.3\/ (2022).<\/span><\/span><\/section>\n
                            [35] NSF International Workshop on Large Scale Neuromorphic Computing. https:\/\/www.nuailab.com\/workshop.html (2022).<\/span><\/span><\/section>\n
                            [36] ONNX: Open Neural Network Exchange. https:\/\/onnx.ai\/ (2019).<\/span><\/span><\/section>\n
                            [37] Orchard, G. et al. in Proc. 2021 IEEE Workshop on Signal Processing Systems (SiPS) 254\u2013259 (IEEE, 2021).<\/span><\/span><\/section>\n
                            [38] Pandit, T. & Kudithipudi, D. in Proc. Neuro-inspired Computational Elements Workshop 1\u20139 (ACM, 2020).<\/span><\/span><\/section>\n
                            [39] Patel, K., Jaworski, P., Hays, J., Eliasmith, C. & DeWolf, T. Adaptive spiking control of a 7 DOF arm. Naval Application in Machine Learning (NAML) Workshop (2022).<\/span><\/span><\/section>\n
                            [40] Pehle, C.-G. & Pedersen, J. E. Norse – a deep learning library for spiking neural networks. Zenodo https:\/\/doi.org\/10.5281\/zenodo.4422024 (2021).<\/span><\/span><\/section>\n
                            [41] Quigley, M. et al. in Proc. ICRA Workshop on Open Source Software 5 (2009).<\/span><\/span><\/section>\n
                            [42] Rathi, N., Agrawal, A., Lee, C., Kosta, A. K. & Roy, K. in Proc. 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) 902\u2013907 (IEEE, 2021). Exploring various spike representations, training mechanisms and event-driven hardware implementations that can make use of the unique features of spiking neural networks for efficient processing.<\/span><\/span><\/section>\n
                            [43] Rockpool – Rockpool Documentation. https:\/\/rockpool.ai\/ (2023).<\/span><\/span><\/section>\n
                            [44] Sawada, J. et al. in SC \u201916: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis 130\u2013141 (IEEE, 2016).<\/span><\/span><\/section>\n
                            [45] Schemmel, J. et al. in Proc. 2010 IEEE International Symposium on Circuits and Systems (ISCAS) 1947\u20131950 (IEEE, 2010).<\/span><\/span><\/section>\n
                            [46] Schemmel, J., Gr\u00fcbl, A., Millner, S. & Friedmann, S. Specification of the HICANN microchip. FACETS project internal documentation (2010).<\/span><\/span><\/section>\n
                            [47] Schmitt, S. et al. in Proc. 2017 International Joint Conference on Neural Networks (IJCNN) 2227\u20132234 (IEEE, 2017). A demonstration of how training on an analogue neuromorphic device (the BrainScaleS wafer-scale system) can correct for anomalies induced by the hardware and achieve high accuracy in emulating deep spiking neural networks.<\/span><\/span><\/section>\n
                            [48] Schrimpf, M. et al. Brain-Score: which artificial neural network for object recognition is most brain-like? Preprint at https:\/\/www.biorxiv.org\/content\/10.1101\/407007v2 (2020).<\/span><\/span><\/section>\n
                            [49] See, H. H. et al. ST-MNIST \u2013 the spiking tactile MNIST neuromorphic dataset. Preprint at https:\/\/arxiv.org\/abs\/2005.04319 (2020).<\/span><\/span><\/section>\n
                            [50] Severa, W., Lehoucq, R., Parekh, O. & Aimone, J. B. in Proc. 2018 International Joint Conference on Neural Networks (IJCNN) 1\u20138 (IEEE, 2018).<\/span><\/span><\/section>\n
                            [51] Sheik, S., Lenz, G., Bauer, F. & Kuepelioglu, N. SINABS: a simple Pytorch based SNN library specialised for Speck. GitHub https:\/\/github.com\/synsense\/sinabs (2024).<\/span><\/span><\/section>\n
                            [52] Soures, N., Helfer, P., Daram, A., Pandit, T. & Kudithipudi, D. in Proc. ICML 2021 Workshop on Theory and Foundation of Continual Learning (2021).<\/span><\/span><\/section>\n
                            [53] Stewart, T. C. A technical overview of the Neural Engineering Framework. Univ. Waterloo110 (2012).<\/span><\/span><\/section>\n
                            [54] Subramoney, A., Nazeer, K. K., Sch\u00f6ne, M., Mayr, C. & Kappel, D. Efficient recurrent architectures through activity sparsity and sparse back-propagation through time. In The Eleventh International Conference on Learning Representations(ICLR) (2023). Spiking event-based architectures going beyond biologically plausible dynamics, achieving state of the art results in language modelling and gesture recognition.<\/span><\/span><\/section>\n
                            [55] Theilman, B. H. et al. in Proc. 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) 779\u2013787 (2023).<\/span><\/span><\/section>\n
                            [56] Vineyard, C. et al. in Proc. Annual Neuro-Inspired Computational Elements Conference 40\u201349 (ACM, 2022).<\/span><\/span><\/section>\n
                            [57] Vitale, A., Renner, A., Nauer, C., Scaramuzza, D. & Sandamirskaya, Y. in Proc. 2021 IEEE International Conference on Robotics and Automation (ICRA) 103\u2013109 (IEEE, 2021).<\/span><\/span><\/section>\n
                            [58] Vogelstein, R. J., Mallik, U. & Cauwenberghs, G. in 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No. 04CH37512) V\u2013V (IEEE, 2004).<\/span><\/span><\/section>\n
                            [59] Widrow, B. Adaptive \u201cAdaline\u201d Neuron Using Chemical \u201cMemistors\u201d (Stanford Univ., 1960).<\/span><\/span><\/section>\n
                            [60] Wu, B., Liu, Z., Yuan, Z., Sun, G. & Wu, C. in Proc. Artificial Neural Networks and Machine Learning \u2013 ICANN 2017 (eds Lintas, A., Rovetta, S., Verschure, P., Villa, A.) 49\u201355 (Springer, 2017).<\/span><\/span><\/section>\n
                            [61] Yik, J. et al. NeuroBench: advancing neuromorphic computing through collaborative, fair and representative benchmarking. Preprint at https:\/\/arxiv.org\/abs\/2304.04640 (2024). A collaborative framework, NeuroBench, from more than 100 co-authors across academic institutions and industry, aims to standardize the evaluation of neuromorphic computing algorithms and systems through a set of inclusive benchmarking tools and guidelines.<\/span><\/span><\/section>\n
                            [62] Zohora, F. T., Karia, V., Daram, A. R., Zyarah, A. M. & Kudithipudi, D. in Proc. 2021 IEEE International Symposium on Circuits and Systems (ISCAS) 1\u20135 (IEEE, 2021).<\/span><\/span><\/section>\n
                            [63] Zohora, F. T., Zyarah, A. M., Soures, N. & Kudithipudi, D. in 2020 IEEE International Symposium on Circuits and Systems (ISCAS) 1\u20135 (IEEE, 2020).<\/span><\/span><\/section>\n
                            [64] Aimone, J. B. (2022). A review of non-cognitive applications for neuromorphic computing. Neuromorph. Comput. Eng. 2.<\/span><\/span><\/section>\n
                            [65] Aimone, J. B. and O. Parekh (2023). The brain\u2019s unique take on algorithms. Nat. Commun. 14.<\/span><\/span><\/section>\n
                            [66] Akopyan, F. (2015). TrueNorth: design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 34.<\/span><\/span><\/section>\n
                            [67] Averbeck, B. B., et al. (2006). Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7.<\/span><\/span><\/section>\n
                            [68] Bai, Y. (2014). Study of multi-level characteristics for 3D vertical resistive switching memory. Sci. Rep. 4.<\/span><\/span><\/section>\n
                            [69] Bartolozzi, C., et al. (2022). Embodied neuromorphic intelligence. Nat. Commun. 13.<\/span><\/span><\/section>\n
                            [70] Bekolay, T. (2014). Nengo: a Python tool for building large-scale functional brain models. Front. Neuroinform. 7.<\/span><\/span><\/section>\n
                            [71] Benjamin, B. V. (2014). Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102.<\/span><\/span><\/section>\n
                            [72] Boahen, K. (2022). Dendrocentric learning for synthetic intelligence. Nature 612.<\/span><\/span><\/section>\n
                            [73] Braun, U. (2015). Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc. Natl Acad. Sci. 112.<\/span><\/span><\/section>\n
                            [74] Brink, S. (2012). A learning-enabled neuron array IC based upon transistor channel models of biological phenomena. IEEE Trans. Biomed. Circuits. Syst. 7.<\/span><\/span><\/section>\n
                            [75] Brown, T. (2020). Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33.<\/span><\/span><\/section>\n
                            [76] Buckley, S. M., et al. (2023). Photonic online learning: a perspective. Nanophotonics 12.<\/span><\/span><\/section>\n
                            [77] Cai, J. (2019). Sparse neuromorphic computing based on spin-torque diodes. Appl. Phys. Lett. 114.<\/span><\/span><\/section>\n
                            [78] Calimera, A., et al. (2013). The human brain project and neuromorphic computing. Funct. Neurol. 28.<\/span><\/span><\/section>\n
                            [79] Ceolini, E. (2020). Hand-gesture recognition based on EMG and event-based camera sensor fusion: a benchmark in neuromorphic computing. Front. Neurosci. 14.<\/span><\/span><\/section>\n
                            [80] Chan, V., et al. (2007). AER EAR: a matched silicon cochlea pair with address event representation interface. IEEE Tran. Circuits Syst. I Regul. Pap. 54.<\/span><\/span><\/section>\n
                            [81] Choquette, J. (2023). NVIDIA Hopper H100 GPU: scaling performance. IEEE Micro 43.<\/span><\/span><\/section>\n
                            [82] Cramer, B., et al. (2020). The Heidelberg spiking data sets for the systematic evaluation of spiking neural networks. IEEE Trans. Neural Netw. Learn. Syst. 33.<\/span><\/span><\/section>\n
                            [83] D\u2019Angelo, G., et al. (2022). Event driven bio-inspired attentive system for the iCub humanoid robot on SpiNNaker. Neuromorph. Comput. Eng. 2.<\/span><\/span><\/section>\n
                            [84] Dalgaty, T. (2021). In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling. Nat. Electron. 4.<\/span><\/span><\/section>\n
                            [85] Davies, M. (2018). Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38.<\/span><\/span><\/section>\n
                            [86] Davies, M. (2019). Benchmarks for progress in neuromorphic computing. Nat. Mach. Intell. 1.<\/span><\/span><\/section>\n
                            [87] Davies, M. (2019). Benchmarks for progress in neuromorphic computing. Nat. Mach. Intell. 1.<\/span><\/span><\/section>\n
                            [88] Davies, M. (2021). Advancing neuromorphic computing with Loihi: a survey of results and outlook. Proc. IEEE 109.<\/span><\/span><\/section>\n
                            [89] Davison, A. P. (2009). PyNN: a common interface for neuronal network simulators. Front. Neuroinform. 2.<\/span><\/span><\/section>\n
                            [90] Disney, A. (2016). DANNA: a neuromorphic software ecosystem. Biol. Inspired Cogn. Archit. 17.<\/span><\/span><\/section>\n
                            [91] Eshraghian, J. K. (2023). Training spiking neural networks using lessons from deep learning. Proc. IEEE 111.<\/span><\/span><\/section>\n
                            [92] Feinberg, I. and I. G. Campbell (2010). Sleep EEG changes during adolescence: an index of a fundamental brain reorganization. Brain Cogn. 72.<\/span><\/span><\/section>\n
                            [93] Gallego, G. (2020). Event-based vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44.<\/span><\/span><\/section>\n
                            [94] Gallo, M. (2023). A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. Nat. Electron. 6.<\/span><\/span><\/section>\n
                            [95] Gleeson, P. (2010). NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comput. Biol. 6.<\/span><\/span><\/section>\n
                            [96] Gleeson, P. (2019). Open Source Brain: a collaborative resource for visualizing, analyzing, simulating, and developing standardized models of neurons and circuits. Neuron 103.<\/span><\/span><\/section>\n
                            [97] Gonzalez, H. A. (2021). Hardware acceleration of EEG-based emotion classification systems: a comprehensive survey. IEEE Trans. Biomed. Circuits Syst. 15.<\/span><\/span><\/section>\n
                            [98] Goodman, D. F. M. and R. Brette (2009). The Brian simulator. Front. Neurosci. 3.<\/span><\/span><\/section>\n
                            [99] Greene, M. R. and B. C. Hansen (2020). Disentangling the independent contributions of visual and conceptual features to the spatiotemporal dynamics of scene categorization. J. Neurosci. 40.<\/span><\/span><\/section>\n
                            [100] Hamilton, K. E., et al. (2018). Sparse hardware embedding of spiking neuron systems for community detection. ACM J. Emerg. Technol. Comput. Syst. 14.<\/span><\/span><\/section>\n
                            [101] Harabi, K. E. (2023). A memristor-based Bayesian machine. Nat. Electron. 6.<\/span><\/span><\/section>\n
                            [102] Hennig, J. A. (2018). Constraints on neural redundancy. Elife 7.<\/span><\/span><\/section>\n
                            [103] Herculano-Houzel, S., et al. (2010). Connectivity-driven white matter scaling and folding in primate cerebral cortex. Proc. Natl Acad. Sci. 107.<\/span><\/span><\/section>\n
                            [104] Hirohata, A. and K. Takanashi (2014). Future perspectives for spintronic devices. J. Phys. D Appl. Phys. 47.<\/span><\/span><\/section>\n
                            [105] Hoefler, T., et al. (2021). Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. 22.<\/span><\/span><\/section>\n
                            [106] Hooker, S. (2021). The hardware lottery. Commun. ACM 64.<\/span><\/span><\/section>\n
                            [107] Iyer, L. R., et al. (2021). Is neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain. Front. Neurosci. 15.<\/span><\/span><\/section>\n
                            [108] J\u00fcrgensen, A. M., et al. (2021). A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain. Neuromorph. Comput. Eng. 1.<\/span><\/span><\/section>\n
                            [109] Kudithipudi, D. (2023). Design principles for lifelong learning AI accelerators. Nat. Electron. 6.<\/span><\/span><\/section>\n
                            [110] Kudithipudi, D., et al. (2016). Design and analysis of a neuromemristive reservoir computing architecture for biosignal processing. Front. Neurosci. 9.<\/span><\/span><\/section>\n
                            [111] LeCun, Y. (1989). Backpropagation applied to handwritten zip code recognition. Neural Comput. 1.<\/span><\/span><\/section>\n
                            [112] Lee, S., et al. (2015). Metal oxide-resistive memory using graphene-edge electrodes. Nat. Commun. 6.<\/span><\/span><\/section>\n
                            [113] Lee, S. H., et al. (2012). Disentangling visual imagery and perception of real-world objects. Neuroimage 59.<\/span><\/span><\/section>\n
                            [114] Lin, C. K. (2018). Programming spiking neural networks on Intel\u2019s Loihi. Computer 51.<\/span><\/span><\/section>\n
                            [115] Liu, B., et al. (2015). Reconfigurable neuromorphic computing system with memristor-based synapse design. Neural Process. Lett. 41.<\/span><\/span><\/section>\n
                            [116] Mack, J. (2020). RANC: reconfigurable architecture for neuromorphic computing. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 40.<\/span><\/span><\/section>\n
                            [117] Mahowald, M. A. and C. Mead (1991). The silicon retina. Sci. Am. 264.<\/span><\/span><\/section>\n
                            [118] Mead, C. (1990). Neuromorphic electronic systems. Proc. IEEE 78.<\/span><\/span><\/section>\n
                            [119] Mehonic, A. and A. J. Kenyon (2022). Brain-inspired computing needs a master plan. Nature 604.<\/span><\/span><\/section>\n
                            [120] Modha, D. S. (2023). Neural inference at the frontier of energy, space, and time. Science 382.<\/span><\/span><\/section>\n
                            [121] Mulaosmanovic, H. (2021). Ferroelectric field-effect transistors based on HfO2: a review. Nanotechnology 32.<\/span><\/span><\/section>\n
                            [122] M\u00fcller, E. (2022). The operating system of the neuromorphic BrainScaleS-1 system. Neurocomputing 501.<\/span><\/span><\/section>\n
                            [123] Neckar, A. (2018). Braindrop: a mixed-signal neuromorphic architecture with a dynamical systems-based programming model. Proc. IEEE 107.<\/span><\/span><\/section>\n
                            [124] Orchard, G., et al. (2015). Converting static image datasets to spiking neuromorphic datasets using saccades. Front. Neurosci. 9.<\/span><\/span><\/section>\n
                            [125] Osborn, L. E. (2018). Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain. Sci. Robot. 3.<\/span><\/span><\/section>\n
                            [126] Painkras, E. (2013). SpiNNaker: a 1-W 18-core system-on-chip for massively-parallel neural network simulation. IEEE J. Solid-State Circuits 48.<\/span><\/span><\/section>\n
                            [127] Paszke, A. (2019). PyTorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32.<\/span><\/span><\/section>\n
                            [128] Payvand, M. (2022). Self-organization of an inhomogeneous memristive hardware for sequence learning. Nat. Commun. 13.<\/span><\/span><\/section>\n
                            [129] Pei, J. (2019). Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature 572.<\/span><\/span><\/section>\n
                            [130] Peng, X., et al. (2020). DNN+ NeuroSim V2. 0: an end-to-end benchmarking framework for compute-in-memory accelerators for on-chip training. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 40.<\/span><\/span><\/section>\n
                            [131] Perot, E., et al. (2020). Learning to detect objects with a 1 megapixel event camera. Adv. Neural Inf. Process. Syst. 33.<\/span><\/span><\/section>\n
                            [132] Raffel, J. I., et al. (1989). A generic architecture for wafer-scale neuromorphic systems. Lincoln Lab. J. 2.<\/span><\/span><\/section>\n
                            [133] Rhodes, O. (2018). sPyNNaker: a software package for running PyNN simulations on SpiNNaker. Front. Neurosci. 12.<\/span><\/span><\/section>\n
                            [134] Richter, O. (2024). DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor. Neuromorph. Comput. Eng. 4.<\/span><\/span><\/section>\n
                            [135] Ritter, P., et al. (2013). The virtual brain integrates computational modeling and multimodal neuroimaging. Brain Connect. 3.<\/span><\/span><\/section>\n
                            [136] Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65.<\/span><\/span><\/section>\n
                            [137] Rossant, C. (2011). Fitting neuron models to spike trains. Front. Neurosci. 5.<\/span><\/span><\/section>\n
                            [138] Rothganger, F., et al. (2014). N2A: a computational tool for modeling from neurons to algorithms. Front. Neural Circuits 8.<\/span><\/span><\/section>\n
                            [139] Rubino, A., et al. (2020). Ultra-low-power FDSOI neural circuits for extreme-edge neuromorphic intelligence. IEEE Trans. Circuits Syst. I Regul. Pap. 68.<\/span><\/span><\/section>\n
                            [140] Sandamirskaya, Y. (2014). Dynamic neural fields as a step toward cognitive neuromorphic architectures. Front. Neurosci. 7.<\/span><\/span><\/section>\n
                            [141] Schrimpf, M. (2020). Integrative benchmarking to advance neurally mechanistic models of human intelligence. Neuron 108.<\/span><\/span><\/section>\n
                            [142] Schuman, C. D. (2022). Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2.<\/span><\/span><\/section>\n
                            [143] Sebastian, A., et al. (2020). Memory devices and applications for in-memory computing. Nat. Nanotechnol. 15.<\/span><\/span><\/section>\n
                            [144] Severa, W., et al. (2019). Training deep neural networks for binary communication with the whetstone method. Nat. Mach. Intell. 1.<\/span><\/span><\/section>\n
                            [145] Song, M. K. (2023). Recent advances and future prospects for memristive materials, devices, and systems. ACS Nano 17.<\/span><\/span><\/section>\n
                            [146] Thakur, C. S. (2018). Large-scale neuromorphic spiking array processors: a quest to mimic the brain. Front. Neurosci. 12.<\/span><\/span><\/section>\n
                            [147] Vitay, J., et al. (2015). ANNarchy: a code generation approach to neural simulations on parallel hardware. Front. Neuroinform. 9.<\/span><\/span><\/section>\n
                            [148] Volzhenin, K., et al. (2022). Multilevel development of cognitive abilities in an artificial neural network. Proc. Natl Acad. Sci. 119.<\/span><\/span><\/section>\n
                            [149] Wan, W. (2022). A compute-in-memory chip based on resistive random-access memory. Nature 608.<\/span><\/span><\/section>\n
                            [150] Wang, W. (2021). Neuromorphic motion detection and orientation selectivity by volatile resistive switching memories. Adv. Intell. Syst. 3.<\/span><\/span><\/section>\n
                            [151] Wong, H. S. P. and S. Salahuddin (2015). Memory leads the way to better computing. Nat. Nanotechnol. 10.<\/span><\/span><\/section>\n
                            [152] Wysocki, B., et al. (2014). Hardware-based artificial neural networks for size, weight, and power constrained platforms. Proc. SPIE 9119.<\/span><\/span><\/section>\n
                            [153] Xie, G. (2020). Redundancy-aware pruning of convolutional neural networks. Neural Comput. 32.<\/span><\/span><\/section>\n
                            [154] Yan, Y. (2019). Efficient reward-based structural plasticity on a SpiNNaker 2 prototype. IEEE Trans. Biomed. Circuits Syst. 13.<\/span><\/span><\/section>\n
                            [155] Yan, Y. (2021). Comparing Loihi with a SpiNNaker 2 prototype on low-latency keyword spotting and adaptive robotic control. Neuromorph. Comput. Eng. 1.<\/span><\/span><\/section>\n
                            [156] Yavuz, E., et al. (2016). GeNN: a code generation framework for accelerated brain simulations. Sci. Rep. 6.<\/span><\/span><\/section>\n
                            [157] Zahoor, F., et al. (2020). Resistive random access memory (RRAM): an overview of materials, switching mechanism, performance, multilevel cell (MLC) storage, modeling, and applications. Nanoscale Res. Lett. 15.<\/span><\/span><\/section>\n
                            [158] Zhu, A. Z. (2018). The multivehicle stereo event camera dataset: an event camera dataset for 3D perception. IEEE Robot. Autom. Lett. 3.<\/span><\/span><\/section>\n
                            [159] Zimmermann, J. (2018). Differentiation of Alzheimer\u2019s disease based on local and global parameters in personalized Virtual Brain models. NeuroImage Clin. 19.<\/span><\/span><\/section>\n
                            [160] Zyarah, A. M. and D. Kudithipudi (2019). Neuromemrisitive architecture of HTM with on-device learning and neurogenesis. ACM J. Emerg. Technol. Comput. Syst. 15.<\/span><\/span><\/section>\n


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