{"id":65302,"date":"2026-01-24T19:48:32","date_gmt":"2026-01-24T11:48:32","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/65302.html"},"modified":"2026-01-24T19:48:32","modified_gmt":"2026-01-24T11:48:32","slug":"%e6%97%b6%e7%a9%ba%e6%b3%a8%e6%84%8f%e5%8a%9b%e8%81%9a%e5%90%88%e5%99%a8%e7%9a%84%e8%84%89%e5%86%b2%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%ef%bc%88staa-snn%ef%bc%89","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/65302.html","title":{"rendered":"\u65f6\u7a7a\u6ce8\u610f\u529b\u805a\u5408\u5668\u7684\u8109\u51b2\u795e\u7ecf\u7f51\u7edc\uff08STAA-SNN\uff09"},"content":{"rendered":"

STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks<\/h2>\n

\u4ee3\u7801：https:\/\/github.com\/GuoLab-UESTC\/SCTFA-SNN<\/p>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u4e00\u3001\u7814\u7a76\u80cc\u666f<\/h3>\n
  • \u80fd\u8017\u4e0e\u751f\u7269\u53ef\u89e3\u91ca\u6027：SNN \u7528 0\/1 \u8109\u51b2\u4e8b\u4ef6\u9a71\u52a8，\u4e58-\u52a0\u8fd0\u7b97\u5927\u91cf\u66ff\u6362\u4e3a\u7eaf\u52a0\u8fd0\u7b97，\u5728 45 nm \u5de5\u827a\u4e0b 0.9 pJ\/AC \u6bd4 4.6 pJ\/MAC \u8282\u80fd 5\u00d7 \u4ee5\u4e0a，\u4e14\u4e0e\u751f\u7269\u795e\u7ecf\u5143\u201c\u53d1\u653e-\u6574\u5408-\u6cc4\u6f0f\u201d\u884c\u4e3a\u4e00\u81f4\u3002<\/li>\n
  • \u6027\u80fd\u74f6\u9888：\u76f4\u63a5\u8bad\u7ec3\u7684\u5927\u6df1\u5ea6 SNN \u5728 CIFAR-100\u3001ImageNet \u4e0a\u4ecd\u6bd4\u540c\u89c4\u6a21 ANN \u4f4e 3\u201310%，\u6210\u4e3a\u843d\u5730\u6700\u5927\u969c\u788d\u3002<\/li>\n
  • \u6ce8\u610f\u529b\u673a\u5236\u5728 ANN \u4e2d\u7684\u6210\u529f，\u542f\u53d1\u5c06\u5176\u5f15\u5165 SNN；\u4f46 SNN \u7684\u201c\u65f6\u7a7a\u6df7\u5408\u3001\u4e8c\u503c\u7a00\u758f\u3001\u591a\u65f6\u95f4\u6b65\u8fed\u4ee3\u201d\u4e09\u5927\u7279\u6027，\u4f7f\u4f20\u7edf\u6ce8\u610f\u529b\u65e0\u6cd5\u76f4\u63a5\u8fc1\u79fb\u3002<\/li>\n

    \u4e8c\u3001\u73b0\u6709\u65b9\u6cd5\u5c40\u9650<\/h3>\n
  • \n

    \u8bad\u7ec3\u65b9\u5f0f
    \n\u2003\u2022 ANN\u2192SNN \u8f6c\u6362：\u9700 64\u2013256 \u65f6\u95f4\u6b65\u8865\u507f\u91cf\u5316\u8bef\u5dee，\u5ef6\u8fdf\u9ad8，\u4e14\u65e0\u6cd5\u5229\u7528\u8109\u51b2\u52a8\u6001\u3002
    \n\u2003\u2022 \u65e0\u76d1\u7763（STDP\/Hebb）：\u96be\u4ee5\u505a\u6df1\u5c42\u7aef\u5230\u7aef\u4f18\u5316\u3002
    \n\u2003\u2022 \u6709\u76d1\u7763 BPTT+\u66ff\u4ee3\u68af\u5ea6：\u68af\u5ea6\u6d88\u5931\u3001\u819c\u7535\u4f4d\u9707\u8361\u3001\u6613\u9677\u5c40\u90e8\u6700\u4f18\u3002<\/p>\n<\/li>\n

  • \n

    \u6ce8\u610f\u529b\u5c1d\u8bd5
    \n\u2003\u2022 TA-SNN\u3001MA-SNN\u3001TCJA-SNN \u7b49\u4ec5\u628a\u201c\u901a\u9053-\u7a7a\u95f4-\u65f6\u95f4\u201d\u62c6\u6210\u72ec\u7acb\u6a21\u5757，\u672a\u5728\u5355\u4e2a\u795e\u7ecf\u5143\u5c42\u5185\u540c\u65f6\u5b8c\u6210\u65f6\u7a7a\u8026\u5408；
    \n\u2003\u2022 \u65e0\u4f4d\u7f6e\u7f16\u7801，\u65f6\u95f4\u987a\u5e8f\u4fe1\u606f\u9760 RNN \u5f0f\u9012\u5f52\u9690\u5f0f\u643a\u5e26，\u6df1\u5ea6\u589e\u52a0\u540e\u8870\u51cf\u4e25\u91cd；
    \n\u2003\u2022 \u672a\u89e3\u51b3\u201c\u591a\u6b65\u7279\u5f81\u56fa\u5316\u201d\u2014\u2014\u8d8a\u6df1\u65f6\u95f4\u6b65\u8d8a\u6613\u65e9\u719f，\u5bfc\u81f4\u7f51\u7edc\u505c\u6b62\u66f4\u65b0\u3002<\/p>\n<\/li>\n

    \u4e09\u3001\u672c\u6587\u521b\u65b0\u70b9<\/h3>\n
  • \u9996\u6b21\u5728 SNN \u5185\u90e8\u63d0\u51fa\u201c\u8109\u51b2\u9a71\u52a8\u7684\u81ea\u6ce8\u610f\u529b\u201d\u2014\u2014\u7528 Global Context (GC) \u5757\u5b9e\u73b0\u7eaf\u5377\u79ef\u5f62\u5f0f\u7684 K\/Q\/V \u81ea\u6ce8\u610f，\u907f\u514d Transformer \u7684\u4e8c\u6b21\u590d\u6742\u5ea6\u3002<\/li>\n
  • \u9996\u6b21\u628a\u201c\u4f4d\u7f6e\u7f16\u7801（PE）\u201d\u5f15\u5165 SNN：\u663e\u5f0f\u628a\u65f6\u95f4\u6b65\u7d22\u5f15\u5d4c\u5165\u5230\u7279\u5f81\u901a\u9053，\u5f25\u8865\u8109\u51b2\u7a00\u758f\u5e26\u6765\u7684\u65f6\u5e8f\u4fe1\u606f\u4e22\u5931\u3002<\/li>\n
  • \u63d0\u51fa\u201c\u6b65\u6ce8\u610f\u529b SA\u201d\u2014\u2014\u5bf9\u6bcf\u4e2a\u65f6\u95f4\u6b65\u7684 4D（T,C,H,W）\u7279\u5f81\u56fe\u91cd\u65b0\u52a0\u6743，\u6291\u5236\u566a\u58f0\u6b65\u3001\u653e\u5927\u5173\u952e\u6b65，\u5b9e\u73b0\u201c\u65f6\u95f4\u7ef4\u5ea6\u8f6f\u9009\u62e9\u201d\u3002<\/li>\n
  • \u63d0\u51fa\u8bad\u7ec3\u7b56\u7565 TSRD：\u5728 (0,T) \u533a\u95f4\u4ee5\u6982\u7387 \u03b2 \u968f\u673a\u4e22\u5f03\u4e0a\u8ff0\u589e\u5f3a\u6a21\u5757，\u56de\u9000\u5230\u7b80\u5355\u76f8\u52a0，\u9632\u6b62\u65e9\u719f\u5c40\u90e8\u6700\u4f18，\u52a0\u901f\u6536\u655b\u3002<\/li>\n
  • \u6574\u4f53\u5373\u63d2\u5373\u7528：GC\/PE\/SA \u4e09\u6a21\u5757+TSRD \u53ef\u5d4c\u5165\u4efb\u4f55\u57fa\u4e8e LIF \u7684 CNN \u7ed3\u6784，\u4e0d\u6539\u53d8\u4e0b\u6e38\u4efb\u52a1\u635f\u5931\u3002<\/li>\n

    \u56db\u3001\u65b9\u6cd5\u8bba\u7ec6\u8282<\/h3>\n
  • \n

    \u81ea\u9002\u5e94 LIF
    \n\u2003\u5c06\u539f\u6765\u56fa\u5b9a\u6cc4\u6f0f\u7cfb\u6570 1\/\u03c4\u3001\u91cd\u7f6e\u7535\u4f4d Vreset \u6539\u6210\u53ef\u5b66\u4e60\u77e9\u9635 M\u3001N，\u8ba9\u6bcf\u5c42\u5174\u594b\/\u6291\u5236\u5f3a\u5ea6\u53ef\u81ea\u52a8\u8c03\u8282\u3002<\/p>\n<\/li>\n

  • \n

    GC \u5757（\u7a7a\u95f4\u81ea\u6ce8\u610f）
    \n\u2003\u8f93\u5165 Xt,n\u2208RC\u00d7H\u00d7W \u2192 1\u00d71 \u5377\u79ef\u751f\u6210 K\/Q\/V \u2192 \u901a\u8fc7 Sigmoid+LN+ReLU \u5f97\u5230\u6743\u91cd Wv\u2208RC\u00d71\u00d71 \u2192 \u4e0e\u539f\u7279\u5f81\u6b8b\u5dee\u76f8\u52a0，\u5b9e\u73b0\u201c\u8109\u51b2-\u9a71\u52a8\u201d\u7684\u81ea\u6ce8\u610f\u3002<\/p>\n<\/li>\n

  • \n

    PE \u5757
    \n\u2003\u751f\u6210\u53ef\u5b66\u4e60\u5411\u91cf Post,n\u2208RC，\u5728\u65f6\u95f4\u8f74\u4e0a\u4e0e\u8f93\u5165 It,n \u76f8\u52a0，\u4f7f\u7f51\u7edc\u77e5\u9053\u201c\u5f53\u524d\u662f\u7b2c\u51e0\u6b65\u201d\u3002<\/p>\n<\/li>\n

  • \n

    SA \u5757（\u6b65\u6ce8\u610f）
    \n\u2003\u5bf9\u6bcf\u6b65\u7279\u5f81 Ut,n \u5148 AvgPool \u538b\u7f29\u2192Conv1\u2192ReLU\u2192Conv2\u2192Sigmoid \u5f97\u5230\u6b65\u6743\u91cd，\u4e0e Ut,n \u9010\u50cf\u7d20\u76f8\u4e58，\u5b9e\u73b0\u201c\u54ea\u4e00\u6b65\u66f4\u91cd\u8981\u201d\u7684\u81ea\u9002\u5e94\u52a0\u6743\u3002<\/p>\n<\/li>\n

  • \n

    STAA-LIF \u524d\u5411\u516c\u5f0f
    \n\u2003Ut,n = GC1(Xt,n) \u2295 GC2(Ht-1,n)\u2003\/\/ \u7a7a\u95f4\u805a\u5408
    \n\u2003Vt,n = SA(Ut,n)\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\/\/ \u65f6\u95f4\u52a0\u6743
    \n\u2003\u518d\u4ee3\u5165\u6807\u51c6 LIF \u66f4\u65b0\u819c\u7535\u4f4d\u4e0e\u8109\u51b2\u3002<\/p>\n<\/li>\n

  • \n

    TSRD
    \n\u2003\u8bad\u7ec3\u9636\u6bb5\u4ee5 \u03b2=0.1 \u6982\u7387\u628a GC\/PE\/SA \u5168\u90e8\u65c1\u8def，\u53ea\u505a\u7b80\u5355\u76f8\u52a0；\u63a8\u7406\u9636\u6bb5\u5168\u90e8\u6253\u5f00\u3002\u7c7b\u4f3c Dropout，\u4f46\u4f5c\u7528\u4e8e\u201c\u6a21\u5757\u7ea7\u201d\u800c\u975e\u201c\u795e\u7ecf\u5143\u7ea7\u201d\u3002<\/p>\n<\/li>\n

    \u4e94\u3001\u5b9e\u9a8c\u8bbe\u7f6e<\/h3>\n

    \u6570\u636e\u96c6：CIFAR-10\/100\u3001ImageNet（\u9759\u6001）；CIFAR10-DVS\u3001DVS128Gesture（\u4e8b\u4ef6\u76f8\u673a）\u3002
    \n\u67b6\u6784：ResNet-18\/19\/20\/34\u3001VGG-13，\u65f6\u95f4\u6b65 T=1\/2\/4\/16\u3002
    \n\u57fa\u7ebf：GLIF\u3001TET\u3001LSG\u3001PFA\u3001MPBN\u3001IM-Loss\u3001Spikformer \u7b49 20 \u4f59\u6761 SOTA\u3002
    \n\u786c\u4ef6：\u5355\u5361 RTX 3090（ImageNet \u7528 8\u00d74090），PyTorch \u5b9e\u73b0\u3002<\/p>\n

    \u516d\u3001\u7ed3\u679c<\/h3>\n
  • \n

    \u9759\u6001\u56fe\u50cf
    \n\u2003\u2022 CIFAR-10：ResNet-19 + T=4 \u8fbe 97.14%（\u21910.42% \u8d85 MPBN），\u4e14 T=1 \u5373\u53ef 96.75%，\u6bd4\u540c\u91cf\u7ea7\u65b9\u6cd5\u5c11\u7528 1\u20133 \u4e2a\u65f6\u95f4\u6b65\u3002
    \n\u2003\u2022 CIFAR-100：ResNet-19 + T=4 \u8fbe 82.05%（\u21911.95% \u8d85 MPBN）\u3002
    \n\u2003\u2022 ImageNet：ResNet-34 + T=4 \u8fbe 70.40%（\u21912.71% \u8d85 SEW-ResNet）\u3002<\/p>\n<\/li>\n

  • \n

    \u4e8b\u4ef6\u76f8\u673a
    \n\u2003\u2022 CIFAR10-DVS：VGG-13 + T=16 \u8fbe 82.10%（\u21911.2% \u8d85 Spikeformer）\u3002
    \n\u2003\u2022 DVS128Gesture：VGG-13 + T=16 \u8fbe 98.61%，\u4e0e LIAF+TA \u6301\u5e73\u4f46\u65f6\u95f4\u6b65\u51cf\u5c11 44%\u3002<\/p>\n<\/li>\n

  • \n

    \u6d88\u878d\u5b9e\u9a8c（CIFAR-100, T=4）
    \n\u2003Baseline
    \n\u2003\u2192 +GC +0.92%
    \n\u2003\u2192 +PE +0.57%
    \n\u2003\u2192 +SA +0.82%
    \n\u2003\u2192 +TSRD +0.32%，\u7d2f\u8ba1\u63d0\u5347 2.80%\u3002
    \n\u2003\u6700\u4f73 dropout \u6982\u7387 \u03b2=0.1；GC \u538b\u7f29\u7cfb\u6570 r=4 \u53ef\u5728\u53c2\u6570\u91cf\u21934\u00d7 \u7684\u540c\u65f6\u4fdd\u6301\u7cbe\u5ea6\u3002<\/p>\n<\/li>\n

  • \n

    \u53ef\u89c6\u5316
    \n\u2003Grad-CAM \u663e\u793a STAA-LIF \u5728 CIFAR10-DVS \u9a6c\u6837\u672c\u4e0a\u805a\u7126\u9a6c\u5934\u3001\u9a6c\u5634\u7b49\u5224\u522b\u533a\u57df，\u800c Vanilla LIF \u4ec5\u7c97\u7565\u8f6e\u5ed3\u3002<\/p>\n<\/li>\n

  • \n

    \u80fd\u8017
    \n\u2003ResNet-20 \u5728 CIFAR-100 \u4e0a\u5355\u5f20 32\u00d732\u3001T=4 \u4ec5 0.366 mJ，\u7ea6\u4e3a\u540c\u7cbe\u5ea6 ANN \u7684 1\/5\u3002<\/p>\n<\/li>\n

    \u4e03\u3001\u610f\u4e49\u4e0e\u5c55\u671b<\/h3>\n
  • \u5b66\u672f\u4ef7\u503c：\u9996\u6b21\u628a\u201c\u7a7a\u95f4-\u81ea\u6ce8\u610f + \u65f6\u5e8f\u4f4d\u7f6e\u7f16\u7801 + \u6b65\u6ce8\u610f\u529b\u201d\u7edf\u4e00\u5728\u8109\u51b2\u795e\u7ecf\u5143\u5185，\u8bc1\u660e SNN \u4e5f\u80fd\u5177\u5907 Transformer \u7c7b\u4f3c\u7684\u52a8\u6001\u9009\u62e9\u80fd\u529b，\u800c\u65e0\u9700\u9ad8\u529f\u8017\u7684\u5bc6\u96c6\u77e9\u9635\u4e58\u6cd5\u3002<\/li>\n
  • \u5de5\u7a0b\u4ef7\u503c：\u5373\u63d2\u5373\u7528\u6a21\u5757 + TSRD \u8bad\u7ec3\u7b56\u7565，\u53ef\u76f4\u63a5\u5d4c\u5165\u73b0\u6709 CNN-SNN \u6846\u67b6，\u5728\u76f8\u540c\u6216\u66f4\u5c11\u65f6\u95f4\u6b65\u4e0b\u83b7\u5f97\u663e\u8457\u7cbe\u5ea6\u63d0\u5347，\u7f29\u77ed\u63a8\u7406\u5ef6\u8fdf，\u964d\u4f4e\u8fb9\u7f18\u7aef\u80fd\u8017\u3002<\/li>\n
  • \u672a\u6765\u65b9\u5411
    \n\u2003\u2022 \u5c06 GC \u5757\u8fdb\u4e00\u6b65\u7a00\u758f\u5316，\u7528\u4e8b\u4ef6\u9a71\u52a8\u5377\u79ef\u6838\u51cf\u5c11\u8ba1\u7b97\u91cf；
    \n\u2003\u2022 \u628a TSRD \u4e0e\u52a8\u6001\u65f6\u95f4\u6b65\u81ea\u9002\u5e94\u7ed3\u5408，\u5b9e\u73b0\u201c\u8bad\u7ec3-\u63a8\u7406\u65f6\u95f4\u6b65\u4e0d\u4e00\u81f4\u201d\u7684\u6781\u81f4\u538b\u7f29；
    \n\u2003\u2022 \u5728\u66f4\u5177\u6311\u6218\u7684\u68c0\u6d4b\u3001\u5206\u5272\u3001\u591a\u6a21\u6001\u4efb\u52a1\u4e0a\u9a8c\u8bc1\u6cdb\u5316\u80fd\u529b；
    \n\u2003\u2022 \u4e0e neuromorphic \u82af\u7247（Loihi\u3001TrueNorth）\u8054\u5408\u90e8\u7f72，\u5b9e\u6d4b\u6beb\u74e6\u7ea7\u529f\u8017\u4e0b\u7684\u7cbe\u5ea6-\u5ef6\u8fdf\u6743\u8861\u3002<\/li>\n","protected":false},"excerpt":{"rendered":"

    STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks\u4ee3\u7801：https:\/\/github.com\/GuoLab-UESTC\/SCTFA-SNN\u4e00\u3001\u7814\u7a76\u80cc\u666f
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