Device‐System End‐to‐End Design of Photonic Neuromorphic Processor Using Reinforcement Learning. Issue 2 (1st December 2022) - Tang, Yingheng; Zamani, Princess Tara; Chen, Ruiyang; Ma, Jianzhu; Qi, Minghao; Yu, Cunxi; Gao, Weilu
Abstract: The incorporation of high‐performance optoelectronic devices into photonic neuromorphic processors can substantially accelerate computationally intensive matrix multiplication operations in machine learning (ML) algorithms. However, the conventional designs of individual devices and system are largely disconnected, and the system optimization is limited to the manual exploration of a small design space. Here, a device‐system end‐to‐end design methodology is reported to optimize a free‐space optical general matrix multiplication (GEMM) hardware accelerator by engineering a spatially reconfigurable array made from chalcogenide phase change materials. With a highly parallelized integrated hardware emulator with experimental information, the design of unit device to directly optimize GEMM calculation accuracy is achieved by exploring a large parameter space through reinforcement learning algorithms, including deep Q‐learning neural network, Bayesian optimization, and their cascaded approach. The algorithm‐generated physical quantities show a clear correlation between system performance metrics and device specifications. Furthermore, physics‐aware training approaches are employed to deploy optimized hardware to the tasks of image classification, materials discovery, and a closed‐loop design of optical ML accelerators. The demonstrated framework offers insights into the end‐to‐end and co‐design of optoelectronic devices and systems with reduced human supervision and domain knowledge barriers. Abstract : A device‐system end‐to‐end design through reinforcement learning is demonstrated to optimize a free‐space optical general matrix multiplication hardware accelerator by engineering a spatially reconfigurable array made from chalcogenide phase change materials without human supervision and domain knowledge. Learning results show a clear device‐system correlation, and optimized hardware can be deployed into a variety of machine learning tasks through physics‐aware training.