Abstract
A neuromorphic computing chip that can imitate the human brain’s ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. However, as a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system.
Highlights
Brain-like artificial neural networks (ANNs) are currently gaining extensive attention as an evolving technology for artificial intelligence, enabling self-learning, speech recognition, and pattern recognition (Jiang et al, 2017; Van and Bohte, 2017; Peng et al, 2019; George et al, 2020; Kumar et al, 2020)
We provide some suggestions and optimization methods for the development of artificial intelligence with self-powered memristive systems
Attempts to realize memristor devices that are integrated with various self-powered systems are a major milestone in the field of improving the energy efficiency of memristive systems
Summary
Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. As a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. An emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. We give a systematic description of selfpowered memristive systems from storage to neuromorphic computing. The review proves a perspective on the application of artificial intelligence with the self-powered memristive system
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