A Reinforcement Learning Methodology for The Search of SRAM CIM-based Accelerator Configuration

被引：0

作者：

Lai, Bo-Xi ^{[1
]}

Huang, Shih-Hsu ^{[1
]}

Kao, Hsu-Yu ^{[1
]}

机构：

[1] Chung Yuan Christian Univ, Dept Elect Engn, Taoyuan, Taiwan

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN, IEEE ICCE-TW 2022 | 2022年

关键词：

Circuit Design; Hardware Design; In-Memory Computing; Machine Learning; Neural Networks;

D O I：

10.1109/ICCE-TAIWAN55306.2022.9869149

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Computing-in-memories (CIM) is recognized as a useful design technique for eliminating the Von Neumann bottleneck. However, there is a need for circuit designers to determine the configuration (i.e., design parameters) of CIM-based accelerators. Note that the configuration has influences on circuit area, throughput, and energy efficiency. In this paper, we focus on the SRAM CIM-based accelerator design. A reinforcement learning methodology is proposed to assist circuit designers to find the most suitable configuration. Experiment data show that the proposed methodology works well in practice.

引用

页码：141 / 142

页数：2

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