Challenges of Computation-in-Memory Circuits for AI Edge Applications

被引：1
作者：
Jhang, Chuan-Jia [1 ]
Chen, Ping-Cheng [2 ]
Chang, Meng-Fan [1 ]
机构：
[1] Natl Tsing Hua Univ, Hsinchu, Taiwan
[2] I Shou Univ, Kaohsiung, Taiwan
来源：
2021 INTERNATIONAL SYMPOSIUM ON VLSI TECHNOLOGY, SYSTEMS AND APPLICATIONS (VLSI-TSA) | 2021年
关键词：
MACRO;
D O I：
10.1109/VLSI-TSA51926.2021.9440045
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In the conventionally von-Neumann computing architecture, the separation of memory and computing units (i.e., the memory-wall bottleneck) poses notable challenges to the development of energy-efficient AI edge devices. Computing-in-memory (CIM) is an emerging approach to breaking through this bottleneck. This paper outlines the background of computing-in-memory macros as well as on-going challenges and trends in the development of this technology.
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页数：2
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