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

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...

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Veröffentlicht in:	2021 58th ACM/IEEE Design Automation Conference (DAC) S. 469 - 474
Hauptverfasser:	Stevens, Jacob R., Venkatesan, Rangharajan, Dai, Steve, Khailany, Brucek, Raghunathan, Anand
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 05.12.2021
Schlagworte:	Deep learning Design automation Hardware hardware/software codesign Natural language processing neural network accelerators Neural networks Software Transformers
Online-Zugang:	Volltext
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