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

doi:10.1109/TCAD.2021.3140194

	Re-FeMAT: A Reconfigurable Multifunctional FeFET-Based Memory Architecture
	Zhang, Xiaoyu 1,2; Liu, Rui 1,3; Song, Tao 1,2; Yang, Yuxin 1,2; Han, Yinhe 1,2; Chen, Xiaoming 1,4
	2022-11-01
发表期刊	IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
ISSN	0278-0070
卷号	41 期号:11 页码:5071-5084
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.
关键词	Convolutional neural network (CNN) ferroelectric field-effect transistor (FeFET) few-shot learning in-memory processing ternary content-addressable memory (TCAM)
DOI	10.1109/TCAD.2021.3140194
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic
WOS记录号	WOS:000877295000128
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19878
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.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Xiangtan Univ, Sch Phys & Optoelect, Xiangtan 411105, Hunan, Peoples R China 4.Beijing Acad Artificial Intelligence, Beijing 100084, 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.