Neuromorphic silicon photonics with 50 GHz tiled matrix multiplication for deep-learning applications

The explosive volume growth of deep-learning (DL) applications has triggered an era in computing, with neuromorphic photonic platforms promising to merge ultra-high speed and energy efficiency credentials the brain-inspired computing primitives. transfer deep neural networks (DNNs) onto silicon (SiPho) architectures requires, however, analog engine that can perform tiled matrix multiplication (TMM) at line rate support DL a large number trainable parameters, similar approach followed by state-of-the-art electronic graphics processing units. Herein, we demonstrate SiPho relies on coherent architecture optical TMM record-high 50 GHz. Its potential applications, where parameters exceeds available hardware dimensions, is highlighted through DNN reliably detect distributed denial-of-service attacks within data center Cohen’s kappa score-based accuracy 0.636.