M3ICRO: Machine learning-enabled compact photonic tensor core based on programmable multi-operand multimode interference

Abstract

Photonic computing shows promise for transformative advancements in machine learning (ML) acceleration, offering ultrafast speed, massive parallelism, and high energy efficiency. However, current photonic tensor core (PTC) designs based on standard optical components hinder scalability and compute density due to their large spatial footprint. To address this, we propose an ultracompact PTC using customized programmable multi-operand multimode interference (MOMMI) devices, named M3ICRO. The programmable MOMMI leverages the intrinsic light propagation principle, providing a single-device programmable matrix unit beyond the conventional computing paradigm of one multiply-accumulate operation per device. To overcome the optimization difficulty of customized devices that often requires time-consuming simulation, we apply ML for optics to predict the device behavior and enable differentiable optimization flow. We thoroughly investigate the reconfigurability and matrix expressivity of our customized PTC and introduce a novel block unfolding method to fully exploit the computing capabilities of a complex-valued PTC for near-universal real-valued linear transformations. Extensive evaluations demonstrate that M3ICRO achieves a 3.5–8.9× smaller footprint, 1.6–4.4× higher speed, 9.9–38.5× higher compute density, 3.7–12× higher system throughput, and superior noise robustness compared to state-of-the-art coherent PTC designs. It also outperforms electronic digital A100 graphics processing unit by 34.8–403× higher throughput while maintaining close-to-digital task accuracy across various ML benchmarks.