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Optical and optoelectronic approaches of performing matrix–vector multiplication (MVM) operations have shown the great promise of accelerating machine learning (ML) algorithms with unprecedented performance. The incorporation of nanomaterials into the system can further improve the device and system performance thanks to their extraordinary properties, but the nonuniformity and variation of nanostructures in the macroscopic scale pose severe limitations for large‐scale hardware deployment. Here, a new optoelectronic architecture is presented, consisting of spatial light modulators and tunable responsivity photodetector arrays made from graphene to perform MVM. The ultrahigh carrier mobility of graphene, high‐power‐efficiency electro‐optic control, and extreme parallelism suggest ultrahigh data throughput and ultralow energy consumption. Moreover, a methodology of performing accurate calculations with imperfect components is developed, laying the foundation for scalable systems. Finally, a few representative ML algorithms are demonstrated, including singular value decomposition, support vector machine, and deep neural networks, to show the versatility and generality of the platform.
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Device‐System End‐to‐End Design of Photonic Neuromorphic Processor Using Reinforcement Learning
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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- PAR ID:
- 10383365
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Laser & Photonics Reviews
- Volume:
- 17
- Issue:
- 2
- ISSN:
- 1863-8880
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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