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Self-Powered Memory Systems

  • Bai Sun
  • , Guangdong Zhou
  • , Ke Xu
  • , Qiang Yu
  • , Shukai Duan
  • Southwest Jiaotong University
  • University of Waterloo
  • Southwest University

科研成果: 期刊稿件文献综述同行评审

24 引用 (Scopus)

摘要

Artificial intelligence memory is expected to acquire, calculate, and analyze a large amount of logical information and data in time to dynamically respond to artificial neural networks. It is the most promising candidate for realizing a new hardware artificial intelligence architecture that mimics biological neural networks. However, the research on artificial intelligence memory is still in the initial stage, and there are some unresolved bottlenecks for the preparation of artificial intelligence memory devices. Such as it require external power supplements for the operating of memory devices, resulting in high power consumption and difficulty in real-time neuromorphic computing. Fortunately, self-powered memory devices can perfectly solve the above problems. In this Review, we have systemically summarized the current development on material, integration, and technology for the self-powered memory application, as well as provide the prospect, suggestion, and optimization method for neuromorphic computing and artificial intelligence with self-powered memory.

源语言英语
页(从-至)1669-1690
页数22
期刊ACS Materials Letters
2
12
DOI
出版状态已出版 - 7 12月 2020
已对外发布

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