Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers

2021 58th ACM/IEEE Design Automation Conference (DAC)(2021)

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摘要
Transformers have transformed the field of natural language processing. Their superior performance is largely attributed to the use of stacked “self-attention” layers, each of which consists of matrix multiplies as well as softmax operations. As a result, unlike other neural networks, the softmax operation accounts for a significant fraction of the total run-time of Transformers. To address this, ...
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关键词
Deep learning,Design automation,Neural networks,Transformers,Hardware,Software,Natural language processing
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