{"id":35702,"date":"2024-12-30T02:00:16","date_gmt":"2024-12-29T18:00:16","guid":{"rendered":"https:\/\/17aitech.com\/?p=35702"},"modified":"2024-12-30T02:00:16","modified_gmt":"2024-12-29T18:00:16","slug":"%e4%bb%8e%e6%9e%b6%e6%9e%84%e3%80%81%e5%b7%a5%e8%89%ba%e5%88%b0%e8%83%bd%e6%95%88%e8%a1%a8%e7%8e%b0%ef%bc%8c%e5%85%a8%e9%9d%a2%e4%ba%86%e8%a7%a3llm%e7%a1%ac%e4%bb%b6%e5%8a%a0%e9%80%9f%ef%bc%8c","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=35702","title":{"rendered":"\u4ece\u67b6\u6784\u3001\u5de5\u827a\u5230\u80fd\u6548\u8868\u73b0\uff0c\u5168\u9762\u4e86\u89e3LLM\u786c\u4ef6\u52a0\u901f\uff0c\u8fd9\u7bc7\u7efc\u8ff0\u5c31\u591f\u4e86"},"content":{"rendered":"

\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:\u4ece\u67b6\u6784\u3001\u5de5\u827a\u5230\u80fd\u6548\u8868\u73b0\uff0c\u5168\u9762\u4e86\u89e3LLM\u786c\u4ef6\u52a0\u901f\uff0c\u8fd9\u7bc7\u7efc\u8ff0\u5c31\u591f\u4e86<\/a><\/p>\n

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\u5927\u8bed\u8a00\u6a21\u578b<\/mark>\uff08LLM\uff09\u7684\u53d1\u5c55\u540c\u65f6\u5f80\u5f80\u4f34\u968f\u7740\u786c\u4ef6\u52a0\u901f\u6280\u672f\u7684\u8fdb\u5316\uff0c\u672c\u6587\u5bf9\u4f7f\u7528 FPGA\u3001ASIC \u7b49\u82af\u7247\u7684\u6a21\u578b\u6027\u80fd\u3001\u80fd\u6548\u8868\u73b0\u6765\u4e86\u4e00\u6b21\u5168\u9762\u6982\u89c8\u3002<\/p>\n<\/section>\n<\/blockquote>\n<\/section>\n

\u5bf9\u4eba\u7c7b\u8bed\u8a00\u8fdb\u884c\u5927\u89c4\u6a21\u5efa\u6a21\u662f\u4e00\u4e2a\u590d\u6742\u7684\u8fc7\u7a0b\uff0c\u7814\u7a76\u4eba\u5458\u82b1\u4e86\u51e0\u5341\u5e74\u7684\u65f6\u95f4\u624d\u5f00\u53d1\u51fa\u6765\u3002\u8fd9\u9879\u6280\u672f\u6700\u65e9\u53ef\u8ffd\u6eaf\u4e8e 1950 \u5e74\uff0c\u5f53\u65f6\u514b\u52b3\u5fb7<\/mark>\u30fb\u9999\u519c\u5c06\u4fe1\u606f\u7406\u8bba\u5e94\u7528\u4e8e\u4eba\u7c7b\u8bed\u8a00\u3002\u4ece\u90a3\u65f6\u8d77\uff0c\u7ffb\u8bd1\u548c\u8bed\u97f3\u8bc6\u522b<\/mark>\u7b49\u4efb\u52a1\u53d6\u5f97\u4e86\u957f\u8db3\u7684\u8fdb\u6b65\u3002<\/p>\n

\u5728\u8fd9\u4e2a\u8fc7\u7a0b\u4e2d\uff0c\u4eba\u5de5\u667a\u80fd<\/mark> (AI) \u548c\u673a\u5668\u5b66\u4e60<\/mark> (ML) \u662f\u6280\u672f\u8fdb\u6b65\u7684\u5173\u952e\u3002ML \u4f5c\u4e3a AI \u7684\u4e00\u4e2a\u5b50\u96c6\uff0c\u5176\u5141\u8bb8\u8ba1\u7b97\u673a\u4ece\u6570\u636e\u4e2d\u8fdb\u884c\u5b66\u4e60\u3002\u4e00\u822c\u6765\u8bf4\uff0cML \u6a21\u578b\u8981\u4e48\u662f\u6709\u76d1\u7763\u7684\uff0c\u8981\u4e48\u662f\u65e0\u76d1\u7763\u7684\u3002<\/p>\n

\u5728\u63a5\u4e0b\u6765\u8981\u4ecb\u7ecd\u7684\u8fd9\u7bc7\u8bba\u6587\u4e2d\u300a Hardware Acceleration of LLMs: A comprehensive survey and comparison \u300b\uff0c\u6765\u81ea\u897f\u963f\u63d0\u5361\u5927\u5b66\u7684\u7814\u7a76\u8005\u91cd\u70b9\u4ecb\u7ecd\u4e86\u6709\u76d1\u7763\u6a21\u578b\u3002<\/p>\n

<\/a><\/p>\n

\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/pdf\/2409.03384<\/p>\n

\u6839\u636e\u8bba\u6587\u4ecb\u7ecd\uff0c\u6df1\u5ea6\u5b66\u4e60<\/mark>\u6a21\u578b\u5206\u4e3a\u751f\u6210\u5f0f\u548c\u5224\u522b\u5f0f\u3002\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd<\/mark>\u662f\u6df1\u5ea6\u5b66\u4e60<\/mark>\u7684\u4e00\u4e2a\u5b50\u96c6\uff0c\u5b83\u4f7f\u7528\u795e\u7ecf\u7f51\u7edc<\/mark>\u6765\u5904\u7406\u6807\u8bb0\u548c\u672a\u6807\u8bb0\u7684\u6570\u636e\u3002\u5927\u578b\u8bed\u8a00\u6a21\u578b<\/mark> (LLM) \u6709\u52a9\u4e8e\u7406\u89e3\u5b57\u7b26\u3001\u5355\u8bcd\u548c\u6587\u672c\u3002<\/p>\n

2017 \u5e74\uff0cTransformer \u5f7b\u5e95\u6539\u53d8\u4e86\u8bed\u8a00\u5efa\u6a21\u3002Transformer \u662f\u4e00\u79cd\u795e\u7ecf\u7f51\u7edc<\/mark>\uff0c\u5b83\u4f7f\u7528\u6ce8\u610f\u529b\u673a\u5236<\/mark>\u5904\u7406\u957f\u671f\u6587\u672c\u4f9d\u8d56\u5173\u7cfb\u3002\u8c37\u6b4c\u4e8e 2017 \u5e74\u521b\u5efa\u4e86\u7b2c\u4e00\u4e2a\u7528\u4e8e\u6587\u672c\u7ffb\u8bd1\u7684 Transformer \u6a21\u578b\u3002Transformer \u6b64\u540e\u4e0d\u65ad\u53d1\u5c55\uff0c\u6539\u8fdb\u4e86\u6ce8\u610f\u529b\u673a\u5236<\/mark>\u548c\u67b6\u6784\u3002\u53d1\u5c55\u5230\u4eca\u5929\uff0cOpenAI \u53d1\u5e03\u7684 ChatGPT \u662f\u4e00\u4e2a\u8457\u540d\u7684 LLM\uff0c\u5b83\u53ef\u4ee5\u9884\u6d4b\u6587\u672c\u5e76\u80fd\u56de\u7b54\u95ee\u9898\u3001\u603b\u7ed3\u6587\u672c\u7b49\u3002<\/p>\n

\u672c\u6587\u5bf9\u4f7f\u7528\u786c\u4ef6\u52a0\u901f\u5668\u6765\u52a0\u901f Transformer \u7f51\u7edc\u6240\u505a\u7684\u4e00\u4e9b\u7814\u7a76\u5de5\u4f5c\u8fdb\u884c\u4e86\u5168\u9762\u7684\u8c03\u67e5\u3002\u8be5\u8c03\u67e5\u4ecb\u7ecd\u4e86\u5df2\u63d0\u51fa\u7684\u6846\u67b6\uff0c\u7136\u540e\u5bf9\u6bcf\u4e2a\u6846\u67b6\u7684\u6280\u672f\u3001\u5904\u7406\u5e73\u53f0\uff08FPGA\u3001ASIC\u3001\u5185\u5b58\u3001GPU\uff09\u3001\u52a0\u901f\u3001\u80fd\u6e90\u6548\u7387\u3001\u6027\u80fd\uff08GOP\uff09\u7b49\u8fdb\u884c\u4e86\u5b9a\u6027\u548c\u5b9a\u91cf\u6bd4\u8f83\u3002<\/p>\n

FPGA \u52a0\u901f\u5668<\/strong><\/p>\n

\u5728\u8fd9\u4e00\u90e8\u5206\u4e2d\uff0c\u4f5c\u8005\u4ee5 A-T \u7f16\u53f7\u7684\u65b9\u5f0f\u5217\u4e3e\u4e86\u6709\u5173 FPGA \u7684\u7814\u7a76\uff0c\u53ef\u8c13\u8c03\u67e5\u7684\u975e\u5e38\u8be6\u7ec6\u3002\u6bcf\u9879\u7814\u7a76\u90fd\u7528\u7b80\u77ed\u7684\u51e0\u53e5\u8bdd\u6982\u62ec\uff0c\u9605\u8bfb\u8d77\u6765\u7b80\u5355\u53c8\u6e05\u6670\u3002\u4e3e\u4f8b\u6765\u8bf4\uff1a<\/p>\n

FTRANS \u30022020 \u5e74\uff0cLi \u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u79cd\u786c\u4ef6\u52a0\u901f\u6846\u67b6 FTRANS\uff0c\u65e8\u5728\u52a0\u901f\u57fa\u4e8e Transformer \u7684\u5927\u89c4\u6a21\u8bed\u8a00\u8868\u793a\u3002FTRANS \u663e\u8457\u63d0\u9ad8\u4e86\u901f\u5ea6\u548c\u80fd\u6548\uff0c\u8d85\u8d8a\u4e86 CPU \u548c GPU \u5b9e\u73b0\uff0c\u5728\u4e00\u7cfb\u5217\u6bd4\u8f83\u540e\u663e\u793a FTRANS \u6bd4\u5176\u4ed6\u65b9\u6848\u5feb 81 \u500d\uff0c\u80fd\u6548\u9ad8 9 \u500d\uff0c\u7279\u522b\u662f\u4e0e\u4f7f\u7528 VCU118 (16nm) \u7684 GPU \u5904\u7406\u5668 RTX5000 \u76f8\u6bd4\u3002\u8be5\u52a0\u901f\u5668\u7684\u6027\u80fd\u901f\u7387\u4e3a 170 GOP\uff0c\u80fd\u6548\u7387\u4e3a 6.8 GOP\/W\u3002<\/p>\n

\u591a\u5934\u6ce8\u610f\u529b\u30022020 \u5e74\uff0cLu \u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e FPGA \u7684\u67b6\u6784\uff0c\u7528\u4e8e\u52a0\u901f Transformer \u7f51\u7edc\u4e2d\u8ba1\u7b97\u6700\u5bc6\u96c6\u7684\u90e8\u5206\u3002\u5728\u4ed6\u4eec\u7684\u5de5\u4f5c\u4e2d\uff0c\u4ed6\u4eec\u4e3a\u4e24\u4e2a\u5173\u952e\u7ec4\u4ef6\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u578b\u786c\u4ef6\u52a0\u901f\u5668\uff0c\u5373\u591a\u5934\u6ce8\u610f\u529b (MHA) ResBlock \u548c\u4f4d\u7f6e\u524d\u9988\u7f51\u7edc (FFN) ResBlock\uff0c\u5b83\u4eec\u662f Transformer \u4e2d\u6700\u590d\u6742\u7684\u4e24\u4e2a\u5c42\u3002\u6240\u63d0\u51fa\u7684\u6846\u67b6\u662f\u5728 Xilinx<\/mark> FPGA \u4e0a\u5b9e\u73b0\u7684\u3002\u6839\u636e\u6027\u80fd\u8bc4\u4f30\uff0c\u4e0e V100 GPU \u76f8\u6bd4\uff0c\u6240\u63d0\u51fa\u7684\u8bbe\u8ba1\u5b9e\u73b0\u4e86 14.6 \u500d\u7684\u52a0\u901f\u3002<\/p>\n

FPGA NPE\u30022021 \u5e74\uff0cKhan \u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u79cd\u7528\u4e8e\u8bed\u8a00\u6a21\u578b<\/mark>\u7684 FPGA \u52a0\u901f\u5668\uff0c\u79f0\u4e3a NPE\u3002NPE \u7684\u80fd\u6e90\u6548\u7387\u6bd4 CPU\uff08i7-8700k\uff09\u9ad8\u7ea6 4 \u500d\uff0c\u6bd4 GPU\uff08RTX 5000\uff09\u9ad8\u7ea6 6 \u500d\u3002<\/p>\n

\u9664\u6b64\u4ee5\u5916\uff0c\u6587\u4e2d\u8fd8\u4ecb\u7ecd\u4e86 ViA \u3001 FPGA DFX \u3001 FPGA OPU \u7b49\u7814\u7a76\uff0c\u8fd9\u91cc\u5c31\u4e0d\u518d\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u3002<\/p>\n

\u57fa\u4e8e CPU \u548c GPU \u7684\u52a0\u901f\u5668<\/strong><\/p>\n

TurboTransformer\u30022021 \u5e74\uff0cJiarui Fang \u548c Yang Yu \u63a8\u51fa\u4e86 TurboTransformers \u52a0\u901f\u5668\uff0c\u8fd9\u662f\u4e00\u79cd\u5728 GPU \u4e0a\u4e13\u4e3a Transformer \u6a21\u578b\u6253\u9020\u7684\u6280\u672f\u3002TurboTransformers \u5728\u53ef\u53d8\u957f\u5ea6\u8f93\u5165\u7684\u5ef6\u8fdf\u548c\u6027\u80fd\u65b9\u9762\u4f18\u4e8e PyTorch \u548c ONNXRuntime\uff0c\u901f\u5ea6\u63d0\u9ad8\u4e86 2.8 \u500d\u3002<\/p>\n

Jaewan Choi\u30022022 \u5e74\uff0c\u7814\u7a76\u5458 Jaewan Choi \u53d1\u8868\u4e86\u9898\u4e3a\u300cAccelerating Transformer Networks through Rewiring of Softmax Layers\u300d\u7684\u7814\u7a76\uff0c\u6587\u4e2d\u63d0\u51fa\u4e86\u4e00\u79cd\u52a0\u901f Transformer \u7f51\u7edc\u4e2d Softmax \u5c42\u7684\u65b9\u6cd5\u3002\u8be5\u7814\u7a76\u5f15\u5165\u4e86\u4e00\u79cd\u91cd\u65b0\u5e03\u7ebf\u6280\u672f\u6765\u52a0\u901f Transformer \u7f51\u7edc\u4e2d\u7684 Softmax \u5c42\uff0c\u968f\u7740 Transformer \u6a21\u578b\u5904\u7406\u66f4\u957f\u7684\u5e8f\u5217\u4ee5\u63d0\u9ad8\u51c6\u786e\u7387<\/mark>\uff0c\u8fd9\u9879\u6280\u672f\u53d8\u5f97\u8d8a\u6765\u8d8a\u91cd\u8981\u3002\u6240\u63d0\u51fa\u7684\u6280\u672f\u5c06 Softmax \u5c42\u5212\u5206\u4e3a\u591a\u4e2a\u5b50\u5c42\uff0c\u66f4\u6539\u6570\u636e\u8bbf\u95ee\u6a21\u5f0f\uff0c\u7136\u540e\u5c06\u5206\u89e3\u7684 Softmax \u5b50\u5c42\u4e0e\u540e\u7eed\u548c\u524d\u9762\u7684\u8fc7\u7a0b\u5408\u5e76\u3002\u8be5\u65b9\u6cd5\u5206\u522b\u5c06 BERT\u3001GPT-Neo\u3001BigBird \u548c Longformer \u5728\u5f53\u524d GPU \u4e0a\u7684\u63a8\u7406\u901f\u5ea6\u52a0\u5feb\u4e86 1.25 \u500d\u30011.12 \u500d\u30011.57 \u500d\u548c 1.65 \u500d\uff0c\u663e\u8457\u51cf\u5c11\u4e86\u7247\u5916\u5185\u5b58\u6d41\u91cf\u3002<\/p>\n

SoftMax\u30022022 \u5e74\uff0cChoi \u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u79cd\u901a\u8fc7\u91cd\u7ec4 Softmax \u5c42\u52a0\u901f Transformer \u7f51\u7edc\u7684\u65b0\u6846\u67b6\u3002Softmax \u5c42\u5c06\u6ce8\u610f\u529b\u77e9\u9635\u7684\u5143\u7d20\u5f52\u4e00\u5316\u4e3a 0 \u5230 1 \u4e4b\u95f4\u7684\u503c\u3002\u6b64\u64cd\u4f5c\u6cbf\u6ce8\u610f\u529b\u77e9\u9635\u7684\u884c\u5411\u91cf\u8fdb\u884c\u3002\u6839\u636e\u5206\u6790\uff0c\u7f29\u653e\u70b9\u79ef\u6ce8\u610f\u529b (SDA) \u5757\u4e2d\u7684 softmax \u5c42\u5206\u522b\u4f7f\u7528\u4e86 BERT\u3001GPT-Neo\u3001BigBird \u548c Longformer \u603b\u6267\u884c\u65f6\u95f4\u7684 36%\u300118%\u300140% \u548c 42%\u3002Softmax \u91cd\u7ec4\u901a\u8fc7\u663e\u8457\u51cf\u5c11\u7247\u5916\u5185\u5b58\u6d41\u91cf\uff0c\u5728 A100 GPU \u4e0a\u5bf9 BERT\u3001GPT-Neo\u3001BigBird \u548c Longformer \u8fdb\u884c\u63a8\u7406\u65f6\u5b9e\u73b0\u4e86\u9ad8\u8fbe 1.25 \u500d\u30011.12 \u500d\u30011.57 \u500d\u548c 1.65 \u500d\u7684\u52a0\u901f\u3002<\/p>\n

\u6b64\u5916\uff0c\u8bba\u6587\u8fd8\u4ecb\u7ecd\u4e86 LightSeq2 \u3001 LLMA \u3001 vLLMs \u7b49\u7814\u7a76\u3002<\/p>\n

ASIC \u52a0\u901f\u5668<\/strong><\/p>\n

A3\u30022020 \u5e74\uff0cHma \u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u9879\u5173\u4e8e Transformer \u7f51\u7edc\u52a0\u901f\u7684\u65e9\u671f\u7814\u7a76\uff0c\u79f0\u4e3a A3 \u3002\u4e0d\u8fc7\uff0c\u7814\u7a76\u4eba\u5458\u6240\u63d0\u51fa\u7684\u65b9\u6848\u5c1a\u672a\u5728 FPGA \u4e0a\u5b9e\u73b0\u3002\u57fa\u4e8e\u6027\u80fd\u8bc4\u4f30\uff0c\u4e0e Intel Gold 6128 CPU \u5b9e\u73b0\u76f8\u6bd4\uff0c\u6240\u63d0\u51fa\u7684\u65b9\u6848\u53ef\u5b9e\u73b0\u9ad8\u8fbe 7 \u500d\u7684\u52a0\u901f\uff0c\u4e0e CPU \u5b9e\u73b0\u76f8\u6bd4\uff0c\u80fd\u6548\u53ef\u63d0\u9ad8 11 \u500d\u3002<\/p>\n

ELSA\u30022021 \u5e74\uff0cHam \u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u79cd\u7528\u4e8e\u52a0\u901f Transformer \u7f51\u7edc\u7684\u786c\u4ef6 – \u8f6f\u4ef6\u534f\u540c\u8bbe\u8ba1\u65b9\u6cd5\uff0c\u79f0\u4e3a Elsa \u3002ELSA \u5927\u5927\u51cf\u5c11\u4e86\u81ea\u6ce8\u610f\u529b<\/mark>\u64cd\u4f5c\u4e2d\u7684\u8ba1\u7b97\u6d6a\u8d39\u3002<\/p>\n

SpAtten\u30022021 \u5e74\uff0cWant \u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u79cd\u7528\u4e8e\u5927\u578b\u8bed\u8a00\u6a21\u578b<\/mark>\u52a0\u901f\u7684\u6846\u67b6 Spatten\u3002SpAtten \u91c7\u7528\u65b0\u9896\u7684 NLP \u52a0\u901f\u65b9\u6848\uff0c\u4ee5\u51cf\u5c11\u8ba1\u7b97\u548c\u5185\u5b58\u8bbf\u95ee\u3002SpAtten \u5206\u522b\u6bd4 GPU\uff08TITAN Xp\uff09\u548c Xeon CPU \u5b9e\u73b0\u4e86 162 \u500d\u548c 347 \u500d\u7684\u52a0\u901f\u3002\u5728\u80fd\u6e90\u6548\u7387\u65b9\u9762\uff0c\u4e0e GPU \u548c CPU \u76f8\u6bd4\uff0cSpAtten \u5b9e\u73b0\u4e86 1193 \u500d\u548c 4059 \u500d\u7684\u8282\u80fd\u3002<\/p>\n

\u5728\u8fd9\u90e8\u5206\uff0c\u4f5c\u8005\u8fd8\u5217\u4e3e\u4e86\u52a0\u901f transformer \u7f51\u7edc\u7684\u65b0\u65b9\u6cd5 Sanger\u3001\u7528\u4e8e\u63d0\u9ad8\u81ea\u7136\u8bed\u8a00\u5904\u7406<\/mark>\u4e2d transformer \u6a21\u578b\u6548\u7387\u7684 AccelTran \u7b49\u591a\u9879\u7814\u7a76\u3002<\/p>\n

\u5185\u5b58\u786c\u4ef6\u52a0\u901f\u5668<\/strong><\/p>\n

ATT\u30022020 \u5e74\uff0cGuo \u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u6ce8\u610f\u529b\u7684\u52a0\u901f\u5668\u52a0\u901f\u65b9\u6cd5\uff0c\u79f0\u4e3a ATT\uff0c\u8be5\u65b9\u6cd5\u57fa\u4e8e\u7535\u963b\u6027 RAM\u3002\u6839\u636e\u6027\u80fd\u8bc4\u4f30\uff0cATT \u4e0e NVIDIA GTX 1080 Ti GPU \u76f8\u6bd4\uff0c\u53ef\u4ee5\u5b9e\u73b0 202 \u500d\u7684\u52a0\u901f\u3002<\/p>\n

ReTransformer\u30022020 \u5e74\uff0cYang \u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u79cd\u7528\u4e8e\u52a0\u901f Transformer \u7684\u5185\u5b58\u6846\u67b6\uff0c\u79f0\u4e3a ReTransformer\u3002ReTransformer \u662f\u4e00\u79cd\u57fa\u4e8e ReRAM \u7684\u5185\u5b58\u67b6\u6784\uff0c\u7528\u4e8e\u52a0\u901f Transformer\uff0c\u5b83\u4e0d\u4ec5\u4f7f\u7528\u57fa\u4e8e ReRAM \u7684\u5185\u5b58\u67b6\u6784\u52a0\u901f Transformer \u7684\u7f29\u653e\u70b9\u79ef\u6ce8\u610f\u529b\uff0c\u800c\u4e14\u8fd8\u901a\u8fc7\u4f7f\u7528\u63d0\u51fa\u7684\u77e9\u9635\u5206\u89e3<\/mark>\u6280\u672f\u907f\u514d\u5199\u5165\u4e2d\u95f4\u7ed3\u679c\u6765\u6d88\u9664\u4e00\u4e9b\u6570\u636e\u4f9d\u8d56\u6027\u3002\u6027\u80fd\u8bc4\u4f30\u8868\u660e\uff0c\u4e0e GPU \u76f8\u6bd4\uff0cReTransformer \u53ef\u4ee5\u5b9e\u73b0\u9ad8\u8fbe 23.21 \u500d\u7684\u52a0\u901f\uff0c\u800c\u76f8\u5e94\u7684\u6574\u4f53\u529f\u7387\u964d\u4f4e\u4e86 1086 \u500d\u3002<\/p>\n

iMCAT\u30022021 \u5e74\uff0cLaguna \u7b49\u4eba\u63d0\u51fa\u4e86\u4e00\u79cd\u7528\u4e8e\u52a0\u901f\u957f\u53e5 Transformer \u7f51\u7edc\u7684\u65b0\u578b\u5185\u5b58\u67b6\u6784\uff0c\u79f0\u4e3a iMCAT\u3002\u8be5\u6846\u67b6\u7ed3\u5408\u4f7f\u7528 XBar \u548c CAM \u6765\u52a0\u901f Transformer \u7f51\u7edc\u3002\u6027\u80fd\u8bc4\u4f30\u8868\u660e\uff0c\u5bf9\u4e8e\u957f\u5ea6\u4e3a 4098 \u7684\u5e8f\u5217\uff0c\u8fd9\u79cd\u65b9\u6cd5\u5b9e\u73b0\u4e86 200 \u500d\u7684\u52a0\u901f\u548c 41 \u500d\u7684\u6027\u80fd\u6539\u8fdb\u3002<\/p>\n

\u9664\u6b64\u4ee5\u5916\uff0c\u8be5\u7ae0\u8282\u8fd8\u4ecb\u7ecd\u4e86 iMCAT \u3001 TransPIM \u3001 iMTransformer \u7b49\u7814\u7a76\u3002<\/p>\n

\u5b9a\u91cf\u6bd4\u8f83<\/strong><\/p>\n

\u4e0b\u8868 I \u5217\u51fa\u4e86\u76ee\u524d\u6240\u6709\u7684\u786c\u4ef6\u52a0\u901f\u5668\u4ee5\u53ca\u5404\u81ea\u7684\u4e3b\u8981\u7279\u6027\uff0c\u5305\u62ec\u52a0\u901f\u5668\u540d\u79f0\u3001\u52a0\u901f\u5668\u7c7b\u578b\uff08FPGA\/ASIC\/In-memory\uff09\u3001\u6027\u80fd\u548c\u80fd\u6548\u3002<\/p>\n

\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u5f53\u63d0\u51fa\u7684\u67b6\u6784\u4e0e CPU\u3001GPU \u8fdb\u884c\u6bd4\u8f83\u65f6\uff0c\u4ee5\u5f80\u7684\u5de5\u4f5c\u4e5f\u4f1a\u63d0\u53ca\u52a0\u901f\u8fd9\u4e00\u6307\u6807\u3002\u4e0d\u8fc7\uff0c\u7531\u4e8e\u6bcf\u79cd\u67b6\u6784\u7684\u57fa\u7ebf\u6bd4\u8f83\u4e0d\u540c\uff0c\u56e0\u800c\u672c\u6587\u53ea\u5c55\u793a\u4e86\u5b83\u4eec\u7684\u7edd\u5bf9\u6027\u80fd\u548c\u80fd\u6548\uff0c\u800c\u6ca1\u6709\u6d89\u53ca\u52a0\u901f\u3002<\/p>\n

<\/a><\/p>\n

\u6027\u80fd\u5b9a\u91cf\u6bd4\u8f83<\/strong><\/p>\n

\u4e0b\u56fe 1 \u5c55\u793a\u4e86\u4e0d\u540c\u5de5\u827a\u6280\u672f\u4e0b\uff0c\u6bcf\u79cd\u52a0\u901f\u5668\u7684\u6027\u80fd\uff1b\u56fe 2 \u5c55\u793a\u4e86\u66f4\u52a0\u6e05\u695a\u7684\u5bf9\u6570\u5c3a\u5ea6\u6027\u80fd\u3002<\/p>\n

<\/a><\/p>\n

<\/a><\/p>\n

\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u91c7\u7528 14nm \u5de5\u827a\u7684 AccelTran\uff08\u670d\u52a1\u5668\uff09\u5b9e\u73b0\u6700\u9ad8\u6027\u80fd\uff0c\u8fbe\u5230\u4e86 372000 GOPs\uff0c\u800c ReTransformer \u6a21\u578b\u7684\u6027\u80fd\u6700\u4f4e\u3002\u6b64\u5916\uff0cViA\u3001Me-ViT \u548c Ftrans \u7b49\u91c7\u7528\u76f8\u540c\u5de5\u827a\u6280\u672f\u7684\u6a21\u578b\u5e76\u6ca1\u6709\u5b9e\u73b0\u76f8\u4f3c\u7684\u6027\u80fd\u3002<\/p>\n

\u4e0d\u8fc7\uff0c\u5bf9\u4e8e\u6ca1\u6709\u91c7\u7528\u76f8\u540c\u5de5\u827a\u6280\u672f\u7684\u52a0\u901f\u5668\uff0c\u5219\u5f88\u96be\u8fdb\u884c\u516c\u5e73\u6bd4\u8f83\u3002\u6bd5\u7adf\uff0c\u5de5\u827a\u6280\u672f\u4f1a\u5bf9\u786c\u4ef6\u52a0\u901f\u5668\u6027\u80fd\u4ea7\u751f\u663e\u8457\u7684\u5f71\u54cd\u3002<\/p>\n

\u80fd\u6548 vs \u5de5\u827a\u6280\u672f<\/strong><\/p>\n

\u4e0b\u56fe 3 \u5c55\u793a\u4e86\u5927\u591a\u6570\u786c\u4ef6\u52a0\u901f\u5668\u7684\u80fd\u6548\uff08GOPs\/W\uff09\u6c34\u5e73\uff0c\u56fe 4 \u5c55\u793a\u4e86\u5bf9\u6570\u5c3a\u5ea6\u5c42\u9762\u7684\u80fd\u6548\u3002\u7531\u4e8e\u5f88\u591a\u67b6\u6784\u6ca1\u6709\u6d4b\u91cf\u80fd\u6548\uff0c\u56e0\u800c\u672c\u6587\u53ea\u5217\u51fa\u4e86\u63d0\u4f9b\u4e86\u80fd\u6548\u7684\u52a0\u901f\u5668\u3002\u5f53\u7136\uff0c\u5f88\u591a\u52a0\u901f\u5668\u91c7\u7528\u4e86\u4e0d\u540c\u7684\u5de5\u827a\u6280\u672f\uff0c\u56e0\u6b64\u5f88\u96be\u8fdb\u884c\u516c\u5e73\u6bd4\u8f83\u3002<\/p>\n

\u7ed3\u679c\u663e\u793a\uff0c\u4ee5\u5185\u5b58\u4e3a\u4e3b\uff08In-Memory \u52a0\u901f\u5668\uff09\u7684\u6a21\u578b\u5177\u6709\u66f4\u597d\u7684\u80fd\u6548\u8868\u73b0\u3002\u539f\u56e0\u5728\u4e8e\u6570\u636e\u4f20\u8f93\u51cf\u5c11\u4e86\uff0c\u5e76\u4e14\u8fd9\u79cd\u7279\u5b9a\u7684\u67b6\u6784\u5141\u8bb8\u6570\u636e\u5728\u5185\u5b58\u4e2d\u76f4\u63a5\u5904\u7406\uff0c\u800c\u4e0d\u9700\u8981\u4ece\u5185\u5b58\u4f20\u8f93\u5230 CPU\u3002<\/p>\n

<\/a><\/p>\n

<\/a><\/p>\n

16nm \u5de5\u827a\u4e0b\u7684\u52a0\u901f\u6bd4\u8f83<\/strong><\/p>\n

\u4e0b\u8868 II \u5c55\u793a\u4e86 16nm \u5de5\u827a\u4e0b\uff0c\u4e0d\u540c\u786c\u4ef6\u52a0\u901f\u5668\u7684\u5916\u63a8\u6027\u80fd\u3002<\/p>\n

<\/a><\/p>\n

\u4e0b\u56fe 5 \u5c55\u793a\u4e86\u5f53\u5728\u76f8\u540c\u7684 16nm \u5de5\u827a\u6280\u672f\u4e0b\u5916\u63a8\u6027\u80fd\u65f6\uff0c\u4e0d\u540c\u786c\u4ef6\u52a0\u901f\u5668\u7684\u7edd\u5bf9\u6027\u80fd\uff0c\u5176\u4e2d AccelTran \u7684\u6027\u80fd\u6c34\u5e73\u6700\u9ad8\u3002<\/p>\n

<\/a><\/p>\n

\u5b9e\u9a8c\u5916\u63a8<\/strong><\/p>\n

\u672c\u6587\u9488\u5bf9 FPGA \u67b6\u6784\u8fdb\u884c\u4e86\u5b9e\u9a8c\u5916\u63a8\uff0c\u5e76\u6d4b\u8bd5\u4e86 20nm\u300128nm\u300140nm\u3001 55nm\u300165nm \u548c 180nm \u5de5\u827a\u4e0b\u6280\u672f\u4e0d\u540c\u7684\u77e9\u9635\u4e58\u6cd5\u4ee3\u7801\uff0c\u4ee5\u9a8c\u8bc1 16nm \u5de5\u827a\u7684\u7406\u8bba\u8f6c\u6362\u6548\u679c\u3002\u7814\u7a76\u8005\u8868\u793a\uff0cFPGA \u6280\u672f\u4e0a\u7684\u77e9\u9635\u4e58\u6cd5\u7ed3\u679c\u6709\u52a9\u4e8e\u5916\u63a8\u4e0d\u540c\u786c\u4ef6\u52a0\u901f\u5668\u5728\u76f8\u540c\u5de5\u827a\u6280\u672f\u4e0a\u7684\u7ed3\u679c\u3002<\/p>\n

\u4e0b\u8868 III \u5c55\u793a\u4e86\u4e0d\u540c FPGA \u8bbe\u5907\u3001\u5de5\u827a\u6280\u672f\u4ee5\u53ca\u77e9\u9635\u4e58\u6cd5 IP \u6838\u7684\u7ed3\u679c\u3002<\/p>\n

<\/a><\/p>\n

\u4e0b\u56fe 6 \u5c55\u793a\u4e86\u6bcf\u79cd FPGA \u8bbe\u5907\u548c\u77e9\u9635\u4e58\u6cd5\u5de5\u827a\u6280\u672f\u7684\u6700\u5927\u65f6\u949f\u9891\u7387\u3002\u7531\u4e8e FPGA \u7684\u6027\u80fd\u4f9d\u8d56\u4e8e\u6700\u5927\u65f6\u949f\u9891\u7387\uff0c\u56e0\u6b64\u5916\u63a8\u6027\u80fd\u4f7f\u5f97\u4e0d\u540c\u5de5\u827a\u6280\u672f\u4e0b\u67b6\u6784\u4e4b\u95f4\u80fd\u591f\u5b9e\u73b0\u516c\u5e73\u6bd4\u8f83\u3002<\/p>\n

<\/a><\/p>\n

\u66f4\u591a\u5b9e\u9a8c\u7ec6\u8282\u8bf7\u53c2\u9605\u539f\u8bba\u6587\u3002<\/p>\n

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