A Novel Method for Scalable VLSI Implementation of Hyperbolic Tangent Function

被引：3
|
作者：
Chandra, Mahesh [1 ]
机构：
[1] NXP Semicond, Noida 201301, India
来源：
IEEE DESIGN & TEST | 2022年 / 39卷 / 01期
关键词：
Deep learning; Table lookup; Biological neural networks; Newton method; Interpolation; Very large scale integration; Taylor series; Artificial neural networks; Neural network; Hyperbolic tangent; nonlinear activation function; VLSI implementation;
D O I：
10.1109/MDAT.2021.3063308
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Editor's notes: Building efficient hardware accelerators to speed up the hardware implementation of deep neural networks (DNNs) continues to present many challenges. This article discusses a novel approach to building hyperbolic tangent and Sigmoid functions that can be easily tuned for different accuracy and precision.-Vivek Chickermane, Cadence
引用
收藏
页码：85 / 91
页数：7
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