Review on the memristor based neuromorphic chips

被引:0
作者
Chen C. [1 ]
Luo C. [1 ]
Liu S. [1 ]
Liu H. [1 ]
机构
[1] College of Electronic Science and Technology, National University of Defense Technology, Changsha
来源
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2023年 / 45卷 / 01期
关键词
acceleration chip; low power consumption; memristor; neuromorphic computing; processing-in-memory;
D O I
10.11887/j.cn.202301001
中图分类号
学科分类号
摘要
In order to master the current development status and development trends of memristor based neuromorphic chips, the existing memristor based neuromorphic chips and architectures were investigated. The memristor array structure and integration process, anterior and posterior neuron circuits, multi-array interconnection topology and data transmission strategy used in the chip, as well as the system simulation and evaluation methods used in the chip design process were compared and analyzed. It is concluded that the current circuit design of memristor based neuromorphic chips still need to solve the problems of limited resistance states, large device parameter fluctuation, complex array peripheral circuits, small integration scale, etc. It is pointed out that the actual application of this type of chip still faces challenges such as the improvement of memristor production process, improvement of development tool support, special instruction set development, and determination of typical traction applications. © 2023 National University of Defense Technology. All rights reserved.
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页码:1 / 14
页数:13
相关论文
共 43 条
[1]  
LECUN Y, BENGIO Y, HINTON G., Deep learning [J], Nature, 521, pp. 436-444, (2015)
[2]  
SZE V, CHEN Y H, YANG T J, Et al., Efficient processing of deep neural networks:a tutorial and survey[J], Proceedings of the IEEE, 105, 12, pp. 2295-2329, (2017)
[3]  
JOUPPI N P, YOUNG C, PATIL N, Et al., In-datacenter performance analysis of a tensor processing unit, Proceedings of the 44th Annual International Symposium on Computer Architecture(ISCA), (2017)
[4]  
LUO T, LIU S L, LI L, Et al., DaDianNao:a neural network supercomputer[J], IEEE Transactions on Computers, 66, 1, pp. 73-88, (2017)
[5]  
PEI J, DENG L, SONG S, Et al., Towards artificial general intelligence with hybrid Tianjic chip architecture[J], Nature, 572, pp. 106-111, (2019)
[6]  
YIN S Y, OUYANG P, YANG J X, Et al., An energy-efficient reconfigurable processor for binary- and ternary-weight neural networks with flexible data bit width[J], IEEE Journal of Solid-State Circuits, 54, 4, pp. 1120-1136, (2019)
[7]  
SCHULLER I K, STEVENS R, PINO R, Et al., Neuromorphic computing:from materials research to systems architecture roundtable, (2015)
[8]  
FURBER S., To build a brain, IEEE Spectrum, 49, 8, pp. 44-49, (2012)
[9]  
SCHUMAN C D, POTOK T E, PATTON R M, Et al., A survey of neuromorphic computing and neural networks in hardware
[10]  
ALIBART F, ZAMANIDOOST E, STRUKOV D B., Pattern classification by memristive crossbar circuits using ex situ and in situ training[J], Nature Communications, 4, (2013)