Reproducible Ozaki‐Based Matrix Multiplication on Photonic Matrix Processors With Algorithm‐Based Fault Tolerance

ABSTRACT

Photonic matrix processors are attracting attention in applications such as AI inference due to their low‐power and low‐latency properties. However, their applicability has been largely limited to low‐precision computation due to inherent stochastic analog noise and device non‐idealities. This limitation creates a large gap between the capabilities of photonic matrix processors and the potential demand for high‐precision computation in scientific computing. In this study, we propose a method to reproduce the results of the Ozaki‐based high‐precision matrix multiplication on low‐precision photonic matrix processors by combining the Ozaki scheme with a simple Algorithm‐Based Fault Tolerance (ABFT). The Ozaki scheme decomposes high‐precision operations into multiple low‐precision computations, while the ABFT introduces algorithm‐level redundancy to detect and correct errors caused by stochastic analog noise. Evaluation results show that ABFT significantly reduces failure probability by orders of magnitude in low‐error regimes, despite the increased number of sub‐computations introduced by the Ozaki decomposition. Furthermore, the overhead of ABFT remains small compared to the total number of photonic tensor‐core invocations, enabling high‐precision computation across a wide range of matrix sizes.