Coherent General-Purpose Photonic Matrix Processor

Abstract

Matrix computations are at the heart of scientific computing, especially in models involving large-scale linear systems. As the scale and complexity of the problems grow, energy-efficient matrix computation becomes critical in these applications. Meanwhile, the advantages of miniaturizing conventional digital electronic processors, predicted by the Dennard scaling, diminish in post-Moore's law era. Analogue photonic devices based on passive and high-throughput interconnects are becoming promising alternatives as next-generation energy-efficient computing units. However, the limited reconfigurability and precision of an analogue photonic computing device make it unsuitable for scientific computing applications. Here, we report a general-purpose analogue photonic matrix processing unit (MPU) based on coherent analogue photonic cores, which perform signed multiplications, with reconfigurability and memory provided by digital electronics. Combined with error management strategies, our photonic MPU can perform tasks conventionally dominated by floating-point digital processors, elevating analog photonic-based platforms toward scientific computing applications. We have experimentally demonstrated its feasibilities in a range of computing tasks, including matrix multiplication and inversion as well as solving finite-difference partial differential equations.