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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.
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This content will become publicly available on March 22, 2026
Free‐space Optical Computing Systems
Abstract Free‐space optical systems are emerging as a hardware platform for high‐throughput and energy‐efficient computing. In this review, the pioneering works are first introduced to lay the foundation for the principles and architectures of systems. The modern hardware implementations of two types of optical computing systems, matrix, and vector multiplication systems and diffractive optical neural network systems, are covered from material, device, and system perspectives. Further, the system deployment to various applications is also discussed. This review serves as an introduction and guideline to the current progress of developing and utilizing free‐space optical computing systems in various domains.
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- PAR ID:
- 10597743
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Annalen der Physik
- ISSN:
- 0003-3804
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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