Device-system Co-design of Photonic Neuromorphic Processor using Reinforcement Learning

TL;DR

This paper presents a co-design methodology integrating device engineering and system optimization for photonic neuromorphic processors, using reinforcement learning to optimize hardware for machine learning tasks.

Contribution

It introduces a novel device-system co-design approach employing reinforcement learning to optimize photonic hardware for neural network acceleration.

Findings

Reinforcement learning effectively optimizes device parameters for system performance.

Physics-aware training improves hardware deployment for various ML tasks.

The framework reduces human supervision in device-system co-design.

Abstract

The incorporation of high-performance optoelectronic devices into photonic neuromorphic processors can substantially accelerate computationally intensive operations in machine learning (ML) algorithms. However, the conventional device design wisdom is disconnected with system optimization. We report a device-system co-design methodology 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 hardware emulator constructed based on experimental information, we demonstrate the design of unit device by optimizing GEMM calculation accuracy via reinforcement learning, including deep Q-learning neural network, Bayesian optimization, and their cascaded approach, which show a clear correlation between system performance metrics and physical device specifications. Furthermore, we employ physics-aware training approaches 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 co-design of optoelectronic devices and systems with reduced human-supervision and domain-knowledge barriers.