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http://hdl.handle.net/10397/105739
Title: | Physics-aware analytic-gradient training of photonic neural networks | Authors: | Zhan, Y Zhang, H Lin, H Chin, LK Cai, H Karim, MF Poenar, DP Jiang, X Mak, MW Kwek, LC Liu, AQ |
Issue Date: | Apr-2024 | Source: | Laser & photonics reviews, Apr. 2024, v. 18, no. 4, 2300445 | Abstract: | Photonic neural networks (PNNs) have emerged as promising alternatives to traditional electronic neural networks. However, the training of PNNs, especially the chip implementation of analytic gradient descent algorithms that are recognized as highly efficient in traditional practice, remains a major challenge because physical systems are not differentiable. Although training methods such as gradient-free and numerical gradient methods are proposed, they suffer from excessive measurements and limited scalability. State-of-the-art in situ training method is also cost-challenged, requiring expensive in-line monitors and frequent optical I/O switching. Here, a physics-aware analytic-gradient training (PAGT) method is proposed that calculates the analytic gradient in a divide-and-conquer strategy, overcoming the difficulty induced by chip non-differentiability in the training of PNNs. Multiple training cases, especially a generative adversarial network, are implemented on-chip, achieving a significant reduction in time consumption (from 31 h to 62 min) and a fourfold reduction in energy consumption, compared to the in situ method. The results provide low-cost, practical, and accelerated solutions for training hybrid photonic-digital electronic neural networks. | Keywords: | On-chip training Optical computing Photonic integrated chip Photonic neural networks |
Publisher: | Wiley-VCH Verlag GmbH & Co. KGaA | Journal: | Laser & photonics reviews | ISSN: | 1863-8880 | EISSN: | 1863-8899 | DOI: | 10.1002/lpor.202300445 | Rights: | © 2024 The Authors. Laser & Photonics Reviews published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. The following publication Y. Zhan, H. Zhang, H. Lin, L. K. Chin, H. Cai, M. F. Karim, D. P. Poenar, X. Jiang, M.-W. Mak, L. C. Kwek, A. Q. Liu, Physics-Aware Analytic-Gradient Training of Photonic Neural Networks. Laser Photonics Rev 2024, 18, 2300445 is available at https://doi.org/10.1002/lpor.202300445. |
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Zhan_Physics_Aware_Analytic‐Gradient.pdf | 2.66 MB | Adobe PDF | View/Open |
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