Energy-efficient computing-in-memory architecture for AI processor: device, circuit, architecture perspective

An artificial intelligence (AI) processor is a promising solution for energy-efficient data processing, including health monitoring and image/voice recognition. However, data movements between compute part and memory induce memory wall and power wall challenges to the conventional computing architec...

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Bibliographic Details
Published in:Science China. Information sciences 2021-06, Vol.64 (6), p.160403, Article 160403
Main Authors: Chang, Liang, Li, Chenglong, Zhang, Zhaomin, Xiao, Jianbiao, Liu, Qingsong, Zhu, Zhen, Li, Weihang, Zhu, Zixuan, Yang, Siqi, Zhou, Jun
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Circuits
Communication
Computation
Computer architecture
Computer Science
Data processing
Energy efficiency
Information Systems and Communication Service
Investigations
Logic
Machine learning
Microprocessors
Neural networks
Optimization techniques
Power management
Review
Static random access memory
Voice recognition
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Summary:An artificial intelligence (AI) processor is a promising solution for energy-efficient data processing, including health monitoring and image/voice recognition. However, data movements between compute part and memory induce memory wall and power wall challenges to the conventional computing architecture. Recently, the memory-centric architecture has been revised to solve the data movement issue, where the memory is equipped with the compute-capable memory technique, namely, computing-in-memory (CIM). In this paper, we analyze the requirement of AI algorithms on the data movement and low power requirement of AI processors. In addition, we introduce the story of CIM and implementation methodologies of CIM architecture. Furthermore, we present several novel solutions beyond traditional analog-digital mixed static random-access memory (SRAM)-based CIM architecture. Finally, recent CIM tape-out studies are listed and discussed.
ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-021-3234-0