Photonic matrix multiplication lights up photonic accelerator and beyond

Hailong Zhou; Jianji Dong; Junwei Cheng; Wenchan Dong; Chaoran Huang; Yichen Shen; Qiming Zhang; Min Gu; Chao Qian; Hongsheng Chen; Zhichao Ruan; Xinliang Zhang

doi:10.1038/s41377-022-00717-8

Photonic matrix multiplication lights up photonic accelerator and beyond

Light Sci Appl. 2022 Feb 3;11(1):30. doi: 10.1038/s41377-022-00717-8.

Authors

Hailong Zhou¹, Jianji Dong², Junwei Cheng¹, Wenchan Dong¹, Chaoran Huang³, Yichen Shen⁴, Qiming Zhang^{5

6}, Min Gu^{5

6}, Chao Qian⁷, Hongsheng Chen⁷, Zhichao Ruan⁸, Xinliang Zhang¹

Affiliations

¹ Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
² Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China. jjdong@hust.edu.cn.
³ Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
⁴ Lightelligence, Hangzhou, 311121, China.
⁵ Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai, 200093, China.
⁶ Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
⁷ Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, ZJU-UIUC Institute, Zhejiang University, Hangzhou, 310027, China.
⁸ Interdisciplinary Center of Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou, 310027, China.

Abstract

Matrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms. Photonic accelerators are designed to accelerate specific categories of computing in the optical domain, especially matrix multiplication, to address the growing demand for computing resources and capacity. Photonic matrix multiplication has much potential to expand the domain of telecommunication, and artificial intelligence benefiting from its superior performance. Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors. In this review, we first introduce the methods of photonic matrix multiplication, mainly including the plane light conversion method, Mach-Zehnder interferometer method and wavelength division multiplexing method. We also summarize the developmental milestones of photonic matrix multiplication and the related applications. Then, we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years. Finally, we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.

Publication types

Review