Physics‐Aware Analytic‐Gradient Training of Photonic Neural Networks

Authors:
raw_author_name: Yuancheng Zhan
raw_affiliation_string: Quantum Science and Engineering Centre (QSec) Nanyang Technological University  Singapore 639798 Singapore
raw_author_name: Hui Zhang
raw_affiliation_string: Institute of Quantum Technology (IQT) The Hong Kong Polytechnic University  Hong Kong 999077 Hong Kong; Quantum Science and Engineering Centre (QSec) Nanyang Technological University  Singapore 639798 Singapore
raw_author_name: Hexiang Lin
raw_affiliation_string: Quantum Science and Engineering Centre (QSec) Nanyang Technological University  Singapore 639798 Singapore
raw_author_name: Lip Ket Chin
raw_affiliation_string: Department of Electrical Engineering City University of Hong Kong  Hong Kong 999077 Hong Kong
raw_author_name: Hong Cai
raw_affiliation_string: Institute of Microelectronics A*STAR  Singapore 138634 Singapore
raw_author_name: Muhammad Faeyz Karim
raw_affiliation_string: School of Electrical &amp; Electronic Engineering Nanyang Technological University  Singapore 639798 Singapore
raw_author_name: Daniel Puiu Poenar
raw_affiliation_string: Quantum Science and Engineering Centre (QSec) Nanyang Technological University  Singapore 639798 Singapore
raw_author_name: Xudong Jiang
raw_affiliation_string: School of Electrical &amp; Electronic Engineering Nanyang Technological University  Singapore 639798 Singapore
raw_author_name: Man‐Wai Mak
raw_affiliation_string: Department of Electronic and Information Engineering The Hong Kong Polytechnic University  Hong Kong 999077 Hong Kong
raw_author_name: Leong Chuan Kwek
raw_affiliation_string: Centre for Quantum Technologies National University of Singapore  Singapore 117543 Singapore; Quantum Science and Engineering Centre (QSec) Nanyang Technological University  Singapore 639798 Singapore
raw_author_name: Ai Qun Liu
raw_affiliation_string: Institute of Quantum Technology (IQT) The Hong Kong Polytechnic University  Hong Kong 999077 Hong Kong; Quantum Science and Engineering Centre (QSec) Nanyang Technological University  Singapore 639798 Singapore

Abstract: 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.