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The attention mechanism is a pivotal element within the Transformerarchitecture, making a substantial contribution to its exceptional performance.Within this attention mechanism, Softmax is an imperative component thatenables the model to assess the degree of correlation between various segmentsof the input. Yet, prior research has shown that Softmax operations cansignificantly increase processing latency and energy consumption in theTransformer network due to their internal nonlinear operations and datadependencies. In this work, we proposed~\textit{Hyft}, a hardware efficientfloating point Softmax accelerator for both training and inference. Hyft aimsto reduce the implementation cost of different nonlinear arithmetic operationsby adaptively converting intermediate results into the most suitable numericformat for each specific operation, leading to reconfigurable accelerator withhybrid numeric format. The evaluation results highlight that Hyft achieves aremarkable $15\times$ reduction in hardware resource utilization and a $20\times$ reduction in processing latency, all while maintaining a negligibleimpact on Transformer accuracy.

Softmax Acceleration with Adaptive Numeric Format for both Training and Inference