News Release

All-optical computing based on convolutional neural networks

Peer-Reviewed Publication

Compuscript Ltd

Figure 1

image: All-optical transcendental equation solver. (A) schematic diagram of the all-optical transcendental equation solver. (B) Top-view SEM image of the all-optical transcendental equation solver, where the scale bar is 100 μm. Here, the white dotted lines mark the five layers for waveform discretization, and the red dotted lines separate the three layers of the optical CNN structure. (C) Output light intensity distribution in the output waveguides (k = 1.67). The arrows in the figure correspond to the locations of the solutions. The horizontal axis is the number of discrete waveguides, the vertical axis on the right represents the output signal intensity, and the vertical axis on the left gives the deviation between the experimental output signal and the theoretical value. (D) A graphic representation of solution deviation. The horizontal axis labels the individual solutions, and the vertical axis represents three values of the parameter k. The shade of the color indicates the magnitude of the deviation. view more 

Credit: OEA

In a new publication from Opto-Electronic Advances; DOI 10.29026/oea.2021.200060 , the research group of Professor Xiaoyong Hu and Professor Qihuang Gong from School of Physics, Peking University, China, propose a new strategy to realize ultrafast and ultralow-energy-consumption all-optical computing chip scheme based on convolutional neural network (CNN), which supports the execution of multiple computing tasks.

With the rapid development of advanced engineering computing, economic data analysis and cloud computing, the demand for ultra-high speed and energy-efficiency computing is growing exponentially. Due to the limited data communication bandwidth between memory and processor, the inherent RC delay of the integrated circuit, and the heat dissipation caused by resistance loss in the electronic circuit, the dominant computing platform, namely the traditional electronic signal processor under von Neumann architecture is difficult to achieve high speed and low energy consumption at the same time.

All optical computing using photons as information carrier provides a potential alternative to the traditional electronic signal processor. However, there is an inherent trade-off that the larger nonlinear coefficient can only be at the expense of the slower response time. This trade-off poses a major challenge to the construction of integrated photonic processors based on von Neumann architecture, which usually requires complex heterogeneous integration of various photonic devices in a single chip. Therefore, it is urgent to explore new architecture and unconventional all-optical computing scheme.

The research group of Prof. Xiaoyong Hu and Prof. Qihuang Gong from School of physics, Peking University, proposes a new strategy to realize ultrafast and ultralow-energy-consumption all-optical computing chip scheme based on convolutional neural network (CNN), which supports the execution of multiple computing tasks. The optical CNN consists of cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments designed to control the amplitude and phase of light in the waveguide branches. As a proof-of-concept, they experimentally implemented the network design through several computation tasks including transcendental equations solvers, multifunctional logic gate operators, and half-adders. The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit. Their approach can be further expanded to offer the possibility of parallel computing using wavelength multiplexing based on non-von Neumann architectures and thus paves a new way for on-chip all-optical computing.



For example, a transcendental equation solver has been achieved to solve the above equation with high accuracy with a maximum deviation less than 5%, and in most cases the deviations are less than 3%. The accuracy of the solution can be improved by increasing the number of output waveguides in theory. Besides excellent solution accuracy, the all-optical equation solver also features ultrafast (The time-of-flight of light passing through the characteristic structure is ~1.3 ps) and energy-efficiency computation (~92 fJ/bit ). Stability analysis of their network further demonstrates its high fault tolerance to defects such as weight deviation and waveguide damage. This work therefore points to a promising direction for next-generation all-optical computing systems.


Article reference: Liao K, Chen Y, Yu ZC, Hu XY, Wang XY et al. All-optical computing based on convolutional neural networks. Opto-Electron Adv 4, 200060 (2021). doi: 10.29026/oea.2021.200060 


Keywords: convolutional neural networks / all-optical computing / mathematical operations / cascaded silicon waveguides

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The research group of Professor Xiaoyong Hu in School of Physics, Peking University, has made many achievements in the research of materials and physics of photonic crystal nanophotonic devices in recent years. Combined with the advantages of photonic crystals and plasmonics, the realization of logic operation and ultra-high speed information processing chips based on photonic crystal platform has been explored. The research contents mainly include novel third-order nonlinear photonic materials; integrated photonic devices and information processing chips based on photonic crystals and metamaterials; topological photonics and applications. Up to now, there have been more than 100 high-level papers published in Nature Photonics, Physical Review Letters, Advances in Optics and Photonics, Advanced Materials, Advanced Functional Materials, Nano Letters, Light-Science & Applications and other international journals. For more information, please refer to their homepage:

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Opto-Electronic Advances (OEA) is a high-impact, open access, peer reviewed monthly SCI journal with an impact factor of 9.682 (Journals Citation Reports for IF 2020). Since its launch in March 2018, OEA has been indexed in SCI, EI, DOAJ, Scopus, CA and ICI databases over the time and expanded its Editorial Board to 33 members from 17 countries and regions (average h-index 46).

The journal is published by The Institute of Optics and Electronics, Chinese Academy of Sciences, aiming at providing a platform for researchers, academicians, professionals, practitioners, and students to impart and share knowledge in the form of high quality empirical and theoretical research papers covering the topics of optics, photonics and optoelectronics.


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