Scalable Back-Propagation-Free Training of Optical Physics-Informed Neural Networks
Keywords: Optical Neural Networks, Physics-Informed Neural Networks, On-Chip Learning, Scalability, Hardware-Software Co-Design
Abstract: Physics-informed neural networks (PINNs) have shown promise in solving partial differential equations (PDEs), with growing interest in their energy-efficient, real-time training on edge devices. Photonic computing offers a potential solution due to its high operation speed.
However, the lack of photonic memory and the large footprint of current photonic devices prevent training realistic-size PINNs on photonic chips. This paper proposes a completely back-propagation-free (BP-free) and highly salable framework to enable training real-size PINNs on silicon photonics platforms. Our approach involves three key innovations: (1) a sparse-grid Stein derivative estimator to avoid the BP in the loss evaluation of a PINN, (2) a dimension-reduced zeroth-order optimization via tensor-train decomposition to achieve better scalability and convergence in BP-free training, and (3) a scalable on-chip photonic PINN training accelerator design using photonic tensor cores. We validate the performance of our numerical methods in both low- and high-dimensional PDE benchmarks. Through circuit simulation based on real device parameters, we further demonstrate the significant performance benefit (e.g., real-time training, huge chip area reduction) of our photonic accelerator. Our framework addresses the fundamental challenges of photonic AI and will enable real-time training of real-size PINNs on photonic chips.
Primary Area: infrastructure, software libraries, hardware, systems, etc.
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Submission Number: 13290
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