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Tang K, Ji X, Liu J, Wang J, Xin Y, Liu J, Wu G, Sun Q, Zeng Z, Xiao R, Madamopoulos N, Chen X, Jiang W. Photonic convolutional neural network with robustness against wavelength deviations. OPTICS EXPRESS 2023; 31:37348-37364. [PMID: 38017866 DOI: 10.1364/oe.497576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 10/09/2023] [Indexed: 11/30/2023]
Abstract
We experimentally explore the practicality of integrated multiwavelength laser arrays (MLAs) for photonic convolutional neural network (PCNN). MLAs represent excellent performance for PCNN, except for imperfect wavelength spacings due to fabrication variation. Therefore, the performance of PCNN with non-ideal wavelength spacing is investigated experimentally and numerically for the first time. The results show that there exists a certain tolerance for wavelength deviation on the degradation of the structural information of the extracted feature map, leading to the robustness of photonic recognition accuracy under non-ideal wavelength spacing. The results suggest that scalable MLAs could serve as an alternative source for the PCNN, to support low-cost optical computing scenarios. For a benchmark classification task of MNIST handwritten digits, the photonic prediction accuracy of 91.2% for stride 1 × 1 scheme using the testing dataset are experimentally obtained at speeds on the order of tera operations per second, compared to 94.14% on computer. The robust performance, flexible spectral control, low cost, large bandwidth and parallel processing capability of the PCNN driven by scalable MLAs may broaden the application possibilities of photonic neural networks in next generation data computing applications.
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Meng X, Zhang G, Shi N, Li G, Azaña J, Capmany J, Yao J, Shen Y, Li W, Zhu N, Li M. Compact optical convolution processing unit based on multimode interference. Nat Commun 2023; 14:3000. [PMID: 37225707 DOI: 10.1038/s41467-023-38786-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/12/2023] [Indexed: 05/26/2023] Open
Abstract
Convolutional neural networks are an important category of deep learning, currently facing the limitations of electrical frequency and memory access time in massive data processing. Optical computing has been demonstrated to enable significant improvements in terms of processing speeds and energy efficiency. However, most present optical computing schemes are hardly scalable since the number of optical elements typically increases quadratically with the computational matrix size. Here, a compact on-chip optical convolutional processing unit is fabricated on a low-loss silicon nitride platform to demonstrate its capability for large-scale integration. Three 2 × 2 correlated real-valued kernels are made of two multimode interference cells and four phase shifters to perform parallel convolution operations. Although the convolution kernels are interrelated, ten-class classification of handwritten digits from the MNIST database is experimentally demonstrated. The linear scalability of the proposed design with respect to computational size translates into a solid potential for large-scale integration.
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Affiliation(s)
- Xiangyan Meng
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, 100190, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Guojie Zhang
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, 100190, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Nuannuan Shi
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, 100190, Beijing, China.
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, 100049, Beijing, China.
| | - Guangyi Li
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, 100190, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - José Azaña
- Institut National de la Recherche Scientifique-Énergie Matériaux et Télécommunications (INRS-EMT), H5A 1K6, Montréal, QC, Canada
| | - José Capmany
- ITEAM Research Institute, Universitat Politècnica de València, 46022, Valencia, Spain
| | - Jianping Yao
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, 511443, Guangzhou, China
- Microwave Photonic Research Laboratory, School of Electrical Engineering and Computer Science, University of Ottawa, K1N 6N5, 25 Templeton Street, Ottawa, ON, Canada
| | - Yichen Shen
- Lightelligence Group, 311121, Hangzhou, China
| | - Wei Li
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, 100190, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Ninghua Zhu
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, 100190, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Ming Li
- State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, 100190, Beijing, China.
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, 100049, Beijing, China.
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Xu Z, Tang K, Ji X, Sun Z, Wang Y, Hong Z, Dai P, Xiao R, Shi Y, Chen X. Experimental demonstration of a photonic convolutional accelerator based on a monolithically integrated multi-wavelength distributed feedback laser. OPTICS LETTERS 2022; 47:5977-5980. [PMID: 37219151 DOI: 10.1364/ol.477624] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 10/23/2022] [Indexed: 05/24/2023]
Abstract
We propose and experimentally demonstrate a simple and energy-efficient photonic convolutional accelerator based on a monolithically integrated multi-wavelength distributed feedback semiconductor laser using the superimposed sampled Bragg grating structure. The photonic convolutional accelerator operates at 44.48 GOPS for one 2 × 2 kernel with a convolutional window vertical sliding stride of 2 and generates 100 images of real-time recognition. Furthermore, a real-time recognition task on the MNIST database of handwritten digits with a prediction accuracy of 84% is achieved. This work provides a compact and low-cost way to realize photonic convolutional neural networks.
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