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

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...

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Veröffentlicht in:	IEEE journal of solid-state circuits Jg. 55; H. 1; S. 203 - 215
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
ISSN:	0018-9200, 1558-173X
Online-Zugang:	Volltext
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