计算机科学
电压
杠杆(统计)
宏
最上等的
动态范围
缩放比例
频率标度
晶体管
计算机硬件
动态电压标度
电子工程
电气工程
工程类
机械工程
纺纱
几何学
数学
机器学习
计算机视觉
程序设计语言
作者
Hidehiro Fujiwara,H. Mori,Wei-Chang Zhao,Mei‐Chen Chuang,Rawan Naous,Chao-Kai Chuang,Takeshi Hashizume,Dar Sun,Chia-Fu Lee,Kerem Akarvardar,Saman Adham,Tan‐Li Chou,Mahmut E. Sinangil,Yuxiao Wang,Yu-Der Chih,Yen-Huei Chen,Hung-Jen Liao,Tsung-Yung Jonathan Chang
出处
期刊:
日期:2022-02-20
卷期号:: 1-3
被引量:192
标识
DOI:10.1109/isscc42614.2022.9731754
摘要
Computing-in-memory (CIM) is being widely explored to minimize power consumption in data movement and multiply-and-accumulate (MAC) for edge-AI devices. Although most prior work focuses on analog-based CIM (ACIM) to leverage the BL charge/discharge operation, the lack of accuracy caused by transistor variation and the ADC is an issue [1]–[3]. In contrast, a digital-based CIM (DCIM) approach realizes enough accuracy and flexibility for various input and weight bit widths [4], while also benefiting from technology scaling. This paper proposes a 64kb DCIM macro using a one-read and one-write (1R1W) 12T bitcell. The DCIM macro can realize simultaneous MAC + write operations and wide range dynamic voltage-frequency scaling (DVFS) due to the 12T cell's 1R1W functionality and low-voltage operation. Further improvements in power-performance-area (PPA) are obtained by optimizing the circuit architecture and layout topology.
科研通智能强力驱动
Strongly Powered by AbleSci AI