GEBA: Gradient-Error-Based Approximation of Activation Functions

IF 3.7 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2023-10-31 DOI:10.1109/JETCAS.2023.3328890

Changmin Ye;Doo Seok Jeong

引用次数: 0

Abstract

Computing-in-memory (CIM) macros aiming at accelerating deep learning operations at low power need activation function (AF) units on the same die to reduce their host-dependency. Versatile CIM macros need to include reconfigurable AF units at high precision and high efficiency in hardware usage. To this end, we propose the gradient-error-based approximation (GEBA) of AFs, which approximates various types of AFs in discrete input domains at high precision. GEBA reduces the approximation error by ca. 49.7%, 67.3%, 81.4%, 60.1% (for sigmoid, tanh, GELU, swish in FP32), compared with the uniform input-based approximation using the same memory as GEBA.

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GEBA：基于梯度误差的激活函数逼近

旨在以低功耗加速深度学习操作的内存计算（CIM）宏需要同一芯片上的激活函数（AF）单元，以减少对主机的依赖。多功能 CIM 宏需要包含可重新配置的高精度 AF 单元，并提高硬件使用效率。为此，我们提出了基于梯度误差的 AF 近似 (GEBA)，可以高精度逼近离散输入域中的各类 AF。与使用与 GEBA 相同内存的基于均匀输入的近似方法相比，GEBA 将近似误差分别降低了约 49.7%、67.3%、81.4% 和 60.1%（对于 FP32 中的 sigmoid、tanh、GELU 和 swish）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal on Emerging and Selected Topics in Circuits and Systems ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

8.50

自引率

2.20%

发文量

期刊介绍： The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.