  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.
