
1.Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
2.Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China
3.Lightelligence, Hangzhou, 311121, China
4.Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai, 200093, China
5.Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
6.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
7.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
Jianji Dong (jjdong@hust.edu.cn)
Published:28 February 2022,
Published Online:03 February 2022,
Received:28 July 2021,
Revised:07 January 2022,
Accepted:13 January 2022
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Zhou, H. L. et al. Photonic matrix multiplication lights up photonic accelerator and beyond. Light: Science & Applications, 11, 158-178 (2022).
Zhou, H. L. et al. Photonic matrix multiplication lights up photonic accelerator and beyond. Light: Science & Applications, 11, 158-178 (2022). DOI: 10.1038/s41377-022-00717-8.
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.
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