首页|A 3D MCAM architecture based on flash memory enabling binary neural network computing for edge AI

A 3D MCAM architecture based on flash memory enabling binary neural network computing for edge AI

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A 3D MCAM architecture based on flash memory enabling binary neural network computing for edge AI
The in-memory computing(IMC)architecture implemented by non-volatile memory units shows great possibilities to break the traditional von Neumann bottleneck.In this paper,a 3D IMC architecture is proposed whose unit is based on a multi-bit content-addressable memory(MCAM).The MCAM unit is com-prised of two 65 nm flash memory and two transistors(2Flash2T),which is reconfigurable and multifunctional for both data write/search and XNOR logic operation.Moreover,the MCAM array can also support the population count(POPCOUNT)operation,which can be beneficial for the training and inference process in binary neural network(BNN)computing.Based on the well-known MNIST dataset,the proposed 3D MCAM architecture shows a 98.63％recognition accuracy and a 300％noise-tolerant performance without significant accuracy deterioration.Our findings can provide the potential for developing highly energy-efficient BNN computing for complex artificial intelligence(AI)tasks based on flash-based MCAM units.

reconfigurablemultifunctionmulti-bit content-addressable memory(MCAM)bitwise opera-tionbinary neural networkedge AIflash memoryin-memory computing(IMC)

Maoying BAI、Shuhao WU、Hai WANG、Hua WANG、Yang FENG、Yueran QI、Chengcheng WANG、Zheng CHAI、Tai MIN、Jixuan WU、Xuepeng ZHAN、Jiezhi CHEN

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School of Information Science and Engineering,Shandong University,Qingdao 266200,China

Center for Spintronic and Quantum Systems,State Key Laboratory for Mechanical Behavior of Materials,School of Materials Science and Engineering,Xi'an Jiaotong University,Xi'an 710000,China

reconfigurable multifunction multi-bit content-addressable memory(MCAM) bitwise opera-tion binary neural network edge AI flash memory in-memory computing(IMC)

2024

10.1007/s11432-023-4019-4

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(12)