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Advances in neuromorphic computing: Expanding horizons for AI development through novel artificial neurons and in-sensor computing
Yubo Yang(杨玉波), Jizhe Zhao(赵吉哲), Yinjie Liu(刘胤洁), Xiayang Hua(华夏扬), Tianrui Wang(王天睿), Jiyuan Zheng(郑纪元), Zhibiao Hao(郝智彪), Bing Xiong(熊兵), Changzheng Sun(孙长征), Yanjun Han(韩彦军), Jian Wang(王健), Hongtao Li(李洪涛), Lai Wang(汪莱), and Yi Luo(罗毅)
Chin. Phys. B, 2024, 33 (3):
030702.
DOI: 10.1088/1674-1056/ad1c58
AI development has brought great success to upgrading the information age. At the same time, the large-scale artificial neural network for building AI systems is thirsty for computing power, which is barely satisfied by the conventional computing hardware. In the post-Moore era, the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits (VLSIC) is challenging to meet the growing demand for AI computing power. To address the issue, technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture, and dealing with AI algorithms much more parallelly and energy efficiently. Inspired by the human neural network architecture, neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices. Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network (SNN), the development in this field has incubated promising technologies like in-sensor computing, which brings new opportunities for multidisciplinary research, including the field of optoelectronic materials and devices, artificial neural networks, and microelectronics integration technology. The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing. This paper reviews firstly the architectures and algorithms of SNN, and artificial neuron devices supporting neuromorphic computing, then the recent progress of in-sensor computing vision chips, which all will promote the development of AI.
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