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Chin. Phys. B, 2020, Vol. 29(7): 078504    DOI: 10.1088/1674-1056/ab90e7
Special Issue: SPECIAL TOPIC — Physics in neuromorphic devices
TOPICAL REVIEW—Physics in neuromorphic devices Prev   Next  

In-memory computing to break the memory wall

Xiaohe Huang(黄晓合)1, Chunsen Liu(刘春森)1,2, Yu-Gang Jiang(姜育刚)2, Peng Zhou(周鹏)1
1 State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China;
2 School of Computer Science, Fudan University, Shanghai 200433, China
Abstract  Facing the computing demands of Internet of things (IoT) and artificial intelligence (AI), the cost induced by moving the data between the central processing unit (CPU) and memory is the key problem and a chip featured with flexible structural unit, ultra-low power consumption, and huge parallelism will be needed. In-memory computing, a non-von Neumann architecture fusing memory units and computing units, can eliminate the data transfer time and energy consumption while performing massive parallel computations. Prototype in-memory computing schemes modified from different memory technologies have shown orders of magnitude improvement in computing efficiency, making it be regarded as the ultimate computing paradigm. Here we review the state-of-the-art memory device technologies potential for in-memory computing, summarize their versatile applications in neural network, stochastic generation, and hybrid precision digital computing, with promising solutions for unprecedented computing tasks, and also discuss the challenges of stability and integration for general in-memory computing.
Keywords:  in-memory computing      non-volatile memory      device technologies      crossbar array  
Received:  05 April 2020      Revised:  02 May 2020      Accepted manuscript online: 
PACS:  85.25.Hv (Superconducting logic elements and memory devices; microelectronic circuits)  
  51.50.+v (Electrical properties)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61925402 and 61851402 ), Science and Technology Commission of Shanghai Municipality, China (Grant No. 19JC1416600), the National Key Research and Development Program of China (Grant No. 2017YFB0405600), and Shanghai Education Development Foundation and Shanghai Municipal Education Commission Shuguang Program, China (Grant No. 18SG01).
Corresponding Authors:  Peng Zhou     E-mail:  pengzhou@fudan.edu.cn

Cite this article: 

Xiaohe Huang(黄晓合), Chunsen Liu(刘春森), Yu-Gang Jiang(姜育刚), Peng Zhou(周鹏) In-memory computing to break the memory wall 2020 Chin. Phys. B 29 078504

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