A review on SRAM-based computing in-memory: Circuits,functions, and applications |
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| Zhiting Lin, Zhongzhen Tong, Jin Zhang, Fangming Wang, Tian Xu, Yue Zhao, Xiulong Wu, Chunyu Peng, Wenjuan Lu, Qiang Zhao, Junning Chen. A review on SRAM-based computing in-memory: Circuits, functions, and applications[J]. Journal of Semiconductors, 2022, 43(3): 031401. doi: 10.1088/1674-4926/43/3/031401 Z T Lin, Z Z Tong, J Zhang, F M Wang, T Xu, Y Zhao, X L Wu, C Y Peng, W J Lu, Q Zhao, J N Chen, A review on SRAM-based computing in-memory: Circuits, functions, and applications[J]. J. Semicond., 2022, 43(3): 031401. doi: 10.1088/1674-4926/43/3/031401.Export: BibTex EndNote |
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| Authors: | Zhiting Lin Zhongzhen Tong Jin Zhang Fangming Wang Tian Xu Yue Zhao Xiulong Wu Chunyu Peng Wenjuan Lu Qiang Zhao Junning Chen |
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| Affiliation: | School of Integrated Circuits, Anhui University, Hefei 230601, China |
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| Abstract: | Artificial intelligence (AI) processes data-centric applications with minimal effort. However, it poses new challenges to system design in terms of computational speed and energy efficiency. The traditional von Neumann architecture cannot meet the requirements of heavily data-centric applications due to the separation of computation and storage. The emergence of computing in-memory (CIM) is significant in circumventing the von Neumann bottleneck. A commercialized memory architecture, static random-access memory (SRAM), is fast and robust, consumes less power, and is compatible with state-of-the-art technology. This study investigates the research progress of SRAM-based CIM technology in three levels: circuit, function, and application. It also outlines the problems, challenges, and prospects of SRAM-based CIM macros. |
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| Keywords: | static random-access memory (SRAM) artificial intelligence (AI) von Neumann bottleneck computing in-memory (CIM) convolutional neural network (CNN) |
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