Advances in neuromorphic computing: Expanding horizons for AI development through novel artificial neurons and in-sensor computing

doi:10.1088/1674-1056/ad1c58

中国物理B ›› 2024, Vol. 33 ›› Issue (3): 30702-030702.doi: 10.1088/1674-1056/ad1c58

所属专题： SPECIAL TOPIC — Post-Moore era: Materials and device physics

Advances in neuromorphic computing: Expanding horizons for AI development through novel artificial neurons and in-sensor computing

Yubo Yang(杨玉波)^1,3,†, Jizhe Zhao(赵吉哲)^1,3,†, Yinjie Liu(刘胤洁)^1,3,†, Xiayang Hua(华夏扬)¹, Tianrui Wang(王天睿)¹, Jiyuan Zheng(郑纪元)², Zhibiao Hao(郝智彪)^1,2, Bing Xiong(熊兵)^1,2, Changzheng Sun(孙长征)^1,2, Yanjun Han(韩彦军)¹, Jian Wang(王健)¹, Hongtao Li(李洪涛)¹, Lai Wang(汪莱)^1,2,‡, and Yi Luo(罗毅)^1,2,§

1 Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;
2 Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China

收稿日期:2023-09-19 修回日期:2023-12-27 接受日期:2024-01-09 出版日期:2024-02-22 发布日期:2024-02-29
通讯作者: Lai Wang, Yi Luo E-mail:wanglai@mail.tsinghua.edu.cn;luoy@tsinghua.edu.cn
基金资助:
Project supported in part by the National Key Research and Development Program of China (Grant No. 2021YFA0716400), the National Natural Science Foundation of China (Grant Nos. 62225405, 62150027, 61974080, 61991443, 61975093, 61927811, 61875104, 62175126, and 62235011), the Ministry of Science and Technology of China (Grant Nos. 2021ZD0109900 and 2021ZD0109903), the Collaborative Innovation Center of Solid-State Lighting and Energy-Saving Electronics and Tsinghua University Initiative Scientific Research Program.

Advances in neuromorphic computing: Expanding horizons for AI development through novel artificial neurons and in-sensor computing

1 Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;
2 Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China

Received:2023-09-19 Revised:2023-12-27 Accepted:2024-01-09 Online:2024-02-22 Published:2024-02-29
Contact: Lai Wang, Yi Luo E-mail:wanglai@mail.tsinghua.edu.cn;luoy@tsinghua.edu.cn
Supported by:
Project supported in part by the National Key Research and Development Program of China (Grant No. 2021YFA0716400), the National Natural Science Foundation of China (Grant Nos. 62225405, 62150027, 61974080, 61991443, 61975093, 61927811, 61875104, 62175126, and 62235011), the Ministry of Science and Technology of China (Grant Nos. 2021ZD0109900 and 2021ZD0109903), the Collaborative Innovation Center of Solid-State Lighting and Energy-Saving Electronics and Tsinghua University Initiative Scientific Research Program.

摘要/Abstract

摘要： 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.

关键词: neuromorphic computing, spiking neural network (SNN), in-sensor computing, artificial intelligence

Abstract: 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.

Key words: neuromorphic computing, spiking neural network (SNN), in-sensor computing, artificial intelligence

中图分类号: (Neural networks, fuzzy logic, artificial intelligence)

07.05.Mh

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). Advances in neuromorphic computing: Expanding horizons for AI development through novel artificial neurons and in-sensor computing[J]. 中国物理B, 2024, 33(3): 30702-030702.

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Advances in neuromorphic computing: Expanding horizons for AI development through novel artificial neurons and in-sensor computing

Advances in neuromorphic computing: Expanding horizons for AI development through novel artificial neurons and in-sensor computing

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