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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 Hyft, a hardware efficient floatingpoint Softmax accelerator for both training and inference. Hyft aims to reducethe implementation cost of different nonlinear arithmetic operations withinsoftmax by adaptively converting intermediate results into the most suitablenumeric format for each specific operation, leading to reconfigurableaccelerator with hybrid numeric format. The evaluation results highlight thatHyft achieves a remarkable 10x reduction in hardware resource utilization and a6x reduction in processing latency, all while maintaining a negligible impacton transformer accuracy.

Hyft: A Reconfigurable Softmax Accelerator with Hybrid Numeric Format for both Training and Inference