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arXiv · 1909.07729

K-TanH: Efficient TanH For Deep Learning

Abhisek Kundu·Alex Heinecke·Dhiraj Kalamkar·Sudarshan Srinivasan·Eric C. Qin·Naveen K. Mellempudi·Dipankar Das·Kunal Banerjee·Bharat Kaul·Pradeep Dubey
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Abstract

We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning. K-TanH consists of parameterized low-precision integer operations, such as, shift and add/subtract (no floating point operation needed) where parameters are stored in very small look-up tables that can fit in CPU registers. K-TanH can work on various numerical formats, such as, Float32 and BFloat16. High quality approximations to other activation functions, e.g., Sigmoid, Swish and GELU, can be derived from K-TanH. Our AVX512 implementation of K-TanH demonstrates $>5\times$ speed up over Intel SVML, and it is consistently superior in efficiency over other approximations that use floating point arithmetic. Finally, we achieve state-of-the-art Bleu score and convergence results for training language translation model GNMT on WMT16 data sets with approximate TanH obtained via K-TanH on BFloat16 inputs.

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Abhisek Kundu, Alex Heinecke, Dhiraj Kalamkar, Sudarshan Srinivasan, Eric C. Qin, Naveen K. Mellempudi, Dipankar Das, Kunal Banerjee, Bharat Kaul, Pradeep Dubey. 2019-09-17. K-TanH: Efficient TanH For Deep Learning. https://arxiv.org/abs/1909.07729

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