LightPro: A Linear Photonic Processor with Full Programmability

Silicon photonics (SiPh) enables integration of optical systems for applications including high-speed data communication, optical I/O, and energy-efficient optical computing for AI accelerators. Photonic AI acceleration aims to improve matrix-vector multiplication (MVM), the most energy-intensive computation in deep neural networks (DNNs). As DNNs grow in complexity, larger photonic MVM networks are required, leading to accumulated optical losses, phase and crosstalk noise, and large device footprints (e.g., Mach–Zehnder Interferometers), limiting scalability and efficiency. This paper proposes a fully programmable linear photonic processor, LightPro, with improved scalability, performance, and footprint. LightPro uses compact, low-loss, and programmable SiPh directional coupler (DC) devices employing phase-change material (PCM) to program the DC’s splitting ratio by thermally inducing phase transitions. Based on this device, a neural architecture search (NAS) and pruning algorithm optimizes the processor for MVM operations. Simulation results show up to 85% footprint reduction and over 50% improvement in power consumption. LightPro performs inference with weight matrices trained on MNIST and Gaussian datasets, showing less than 5% drop in accuracy when scaling. Prototyping with the iPronics SmartLight processor demonstrates LightPro’s efficiency, computational accuracy, and feasibility for next-generation photonic AI accelerators