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Re-FeMAT: A Reconfigurable Multifunctional FeFET-Based Memory Architecture

文献类型：期刊论文

作者Zhang, Xiaoyu3,4; Liu, Rui2,4; Song, Tao3,4; Yang, Yuxin3,4; Han, Yinhe3,4; Chen, Xiaoming1,4
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2022-11-01
卷号41期号:11页码:5071-5084
ISSN号0278-0070
关键词Convolutional neural network (CNN) ferroelectric field-effect transistor (FeFET) few-shot learning in-memory processing ternary content-addressable memory (TCAM)
DOI10.1109/TCAD.2021.3140194
英文摘要Most of current processing-in-memory (PIM) architectures are application specific, that is, they can only accelerate particular functions, e.g., matrix-vector dot product for neural network acceleration. However, practical applications usually involve various functions. In order to accelerate different functions, various accelerators, and dedicated circuits have been proposed. In this work, by exploring the similarities among some commonly used dedicated circuits, we adopt ferroelectric field-effect transistors (FeFETs) to build a reconfigurable multifunctional memory architecture named Re-FeMAT. Re-FeMAT is composed of multiple processing elements (PEs). Each PE is not only a nonvolatile memory array, but also can perform logic operations (i.e., the PIM mode), convolutions (i.e., the binary convolutional neural network and the convolutional neural network (CNN) acceleration mode) and content search (i.e., the ternary content-addressable memory (TCAM) mode) without changing the circuit structure. Re-FeMAT can support applications that require multiple functions. As an example, by configuring different PEs to different working modes and using a simulated annealing algorithm or a tabu search algorithm to optimize the task-PE assignment, Re-FeMAT can completely accelerate few-shot learning applications. Our simulation results based on a calibrated FeFET model show that the proposed Re-FeMAT architecture achieves better performance and power efficiency than the previous FeMAT architecture. Compared with FeFET-based single-functional circuits, though the power dissipation of Re-FeMAT is higher in some modes, the power-delay product is still smaller. Compared with a state-of-the-art FeFET-based multifunctional accelerator named attention-in-memory, Re-FeMAT achieves lower power, latency, and energy when accelerating a complete few-shot learning task.
资助项目National Key Research and Development Program of China[2018YFA0701500] ; Key Research Program of Frontier Sciences, Chinese Academy of Sciences[ZDBS-LY-JSC012] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB44000000] ; National Natural Science Foundation of China[62122076] ; National Natural Science Foundation of China[61834006] ; National Natural Science Foundation of China[62025404] ; Youth Innovation Promotion Association CAS ; Beijing Academy of Artificial Intelligence (BAAI)
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000877295000128
源URL[http://119.78.100.204/handle/2XEOYT63/19877]  
专题中国科学院计算技术研究所期刊论文
通讯作者Chen, Xiaoming
作者单位1.Beijing Acad Artificial Intelligence, Beijing 100084, Peoples R China
2.Xiangtan Univ, Sch Phys & Optoelect, Xiangtan 411105, Hunan, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Xiaoyu,Liu, Rui,Song, Tao,et al. Re-FeMAT: A Reconfigurable Multifunctional FeFET-Based Memory Architecture[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2022,41(11):5071-5084.
APA Zhang, Xiaoyu,Liu, Rui,Song, Tao,Yang, Yuxin,Han, Yinhe,&Chen, Xiaoming.(2022).Re-FeMAT: A Reconfigurable Multifunctional FeFET-Based Memory Architecture.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,41(11),5071-5084.
MLA Zhang, Xiaoyu,et al."Re-FeMAT: A Reconfigurable Multifunctional FeFET-Based Memory Architecture".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 41.11(2022):5071-5084.

入库方式： OAI收割

来源：计算技术研究所

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