Shi Jiajuan, Wang Zhongqiang, Tao Ye, Xu Haiyang, Zhao Xiaoning, Lin Ya, Liu Yichun
Key Laboratory for Ultraviolet Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, Changchun, China.
School of Science, Changchun University of Science and Technology, Changchun, China.
Front Neurosci. 2021 Mar 31;15:662457. doi: 10.3389/fnins.2021.662457. eCollection 2021.
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.
一种能够模仿人类大脑同时处理多种类型数据能力的神经形态计算芯片，可能会从根本上革新并改进一直备受诟病的冯·诺依曼计算机架构。忆阻器件因其在固有低电压下运行、采用多位存储且制造成本效益高，而成为构建神经形态智能系统的最佳硬件单元之一。然而，作为一种无源器件，忆阻器单元需要外部能量来运行，导致功耗高且电路结构复杂。最近，一种新兴的自供电忆阻系统，主要由一个忆阻器和一个纳米发电机组成，有潜力完美解决上述问题。因其无功耗运行的优势，它引起了极大的关注。在这篇综述中，我们对自供电忆阻系统从存储到神经形态计算进行了系统描述。该综述还给出了自供电忆阻系统在人工智能应用方面的展望。
