Coherent Photonic Crossbar Arrays for Large-Scale Matrix-Matrix Multiplication

Advances in deep learning research over the past decade have been enabled by an increasingly unsustainable demand for compute power. This trend has dramatically outpaced the slowing growth in the performance and efficiency of electronic computing hardware. Here, we propose a hybrid photonic-electronic computing architecture which leverages a photonic crossbar array and homodyne detection to perform large-scale coherent matrix-matrix multiplication. This approach bypasses the requirements of high-speed electronic readout and frequent reprogramming of photonic weights which significantly reduces energy consumption and latency in the limit of large matrices-two major factors limiting efficiency for many analog computing approaches.