Embedded 1-Mb ReRAM-Based Computing-in- Memory Macro With Multibit Input and Weight for CNN-Based AI Edge Processors

• Published in: IEEE journal of solid-state circuits Vol. 55; no. 1; pp. 203 - 215
• Main Authors: Xue, Cheng-Xin, Chen, Wei-Hao, Liu, Je-Syu, Li, Jia-Fang, Lin, Wei-Yu, Lin, Wei-En, Wang, Jing-Hong, Wei, Wei-Chen, Huang, Tsung-Yuan, Chang, Ting-Wei, Chang, Tung-Cheng, Kao, Hui-Yao, Chiu, Yen-Cheng, Lee, Chun-Ying, King, Ya-Chin, Lin, Chrong-Jung, Liu, Ren-Shuo, Hsieh, Chih-Cheng, Tang, Kea-Tiong, Chang, Meng-Fan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Artificial intelligence (AI); Circuit design; CNN edge processors; Common Information Model (computing); Computation; computing-in-memory (CIM); Energy efficiency; Kernel; Nonvolatile memory; Periodic structures; Program processors; ReRAM; Resistance; sense amplifier; Sense amplifiers; Weight

Computing-in-memory (CIM) based on embedded nonvolatile memory is a promising candidate for energy-efficient multiply-and-accumulate (MAC) operations in artificial intelligence (AI) edge devices. However, circuit design for NVM-based CIM (nvCIM) imposes a number of challenges, including an area-late...