Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: K-TanH: Efficient TanH For Deep Learning   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.

11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: econ
24
The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th