CENTRALE LYON - PhD Silicon Photonics for Scalable Energy-Efficient AI Hardware Accelerators

The rapid expansion of artificial intelligence (AI) applications across cloud and edge environments is driving the need for highly efficient hardware capable of supporting increasingly demanding workloads. Among these, matrix multiplication – particularly General Matrix Multiplication (GEMM) – remains the dominant computational kernel in modern AI models, including deep neural networks and transformer architectures. Conventional electronic accelerators, while highly optimized, face growing challenges related to energy consumption, data movement, and scalability.

Silicon photonics has emerged as a promising technology to address these limitations by enabling highbandwidth, low-latency, and potentially energy-efficient computation through optical signal processing. In particular, recent advances in photonic computing leveraging phase-change materials (PCM) offer new opportunities for implementing reconfigurable and non-volatile computational primitives directly in the optical domain.

This PhD is part of the CAMELIA program (ASIC and Numérique agencies) within the targeted project LEAF, and aims to explore a hybrid photonic–electronic computing paradigm for AI acceleration. The central objective is to design and evaluate silicon photonics-based architectures for efficient GEMM operations, building on recent work demonstrating a reconfigurable photonic GEMM architecture based on PCM and stochasti