News Release

Light accelerates the matrix multiplication for artificial intelligence

Peer-Reviewed Publication

Light Publishing Center, Changchun Institute of Optics, Fine Mechanics And Physics, CAS

Working principle of the photonic accelerator

image: a, concept of photonic accelerator with photonic matrix multiplication. b, methods for photonic matrix multiplication. c, schematic diagram of the optoelectronic-hybrid AI computing chip framework. view more 

Credit: by Hailong Zhou, Jianji Dong Junwei Cheng, Wenchan Dong, Chaoran Huang, Yichen Shen, Qiming Zhang, Min Gu, Chao Qian, Hongsheng Chen, Zhichao Ruan, and Xinliang Zhang

There has been an ever-growing demand for artificial intelligence and fifth-generation communications globally, resulting in very large computing power and memory requirements. The slowing down or even failure of Moore's law makes it increasingly difficult to improve their performance and energy efficiency by relying on advanced semiconductor technology. Optical devices can have a super-large bandwidth and low power consumption. And light has an ultrahigh frequency up to 100 THz and multiple degrees of freedom in their quantum state, making optical computing one of the most competitive candidates for high-capacity and low-latency matrix information processing in the “More than Moore” era. In recent years, photonic matrix multiplication has been developed rapidly and widely used in photonic acceleration fields such as optical signal processing, artificial intelligence, and photonic neural network.  These applications based on matrix multiplication show the great potential and opportunities in photonic accelerator.

In a new review published in Light Science & Application, a team of scientists, led by Professor Jianji Dong from Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology in China and co-workers have introduced the methods of photonic matrix multiplication, and summarize the developmental milestones of photonic matrix multiplication and the related applications. Then, their detailed advances in applications to optical signal processing and artificial neural networks in recent years were reviewed. Comments on the challenges and perspectives of photonic matrix multiplication and photonic acceleration were also discussed


The paper reviewed and discussed the progress of photonic accelerators from a unique viewpoint of photonic matrix multiplication. These scientists summarize the main content of this review:

“The methods for photonic matrix-vector multiplications (MVMs) mainly fall into three categories: the plane light conversion (PLC) method, Mach–Zehnder interferometer (MZI) method and wavelength division multiplexing (WDM) method.”


“The photonic matrix multiplication network itself can be used as a general linear photonic loop for photonic signal processing. In recent years, MVM has been developed as a powerful tool for a variety of photonic signal processing methods.”


“AI technology has been widely used in various electronics industries, such as for deep-learning-based speech recognition and image processing. MVM, as the basic building block of ANNs, occupies most of the computing tasks, such as over 80% for GoogleNet and OverFeat models. Improving the MVM performance is one of the most effective means for ANN acceleration. Compared with electrical computing, optical computing is poor at data storage and flow control, and the low efficiency of optical nonlinearities limits the applications in nonlinear computation, such as activation functions. While it has significant advantages on massively parallel computing through multiplexing strategies of wavelength, mode and polarization, extremely high data modulation speeds up to 100 GHz. Hence, photonic networks are quite good at MVM. The combination of optical computing and AI is expected to realize intelligent photonic processors and photonic accelerators. In recent years, AI technology has also seen rapid developments in the field of optics.”


“In general, photonic computing has obvious advantages in terms of signal rate, latency, power consumption and computing density, and its accuracy is generally lower than that of electrical computing.”


“Before the all-optical ANNs are mature, especially in optical nonlinear effect and optical cascade, optoelectronic-hybrid AI is a more practical and more competitive candidate for deep ANNs. Therefore, the development of a highly efficient and dedicated optoelectronic-hybrid AI hardware chip system is one of the core research routes of photonic AI.”

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