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 with the brain-inspired computing primitives. The transfer of deep neural networks (DNNs) onto silicon photonic (SiPho) architectures requires, however, an analog computing engine that can perform tiled matrix multiplication (TMM) at line rate to support DL applications with a large number of trainable parameters, similar to the approach followed by state-of-the-art electronic graphics processing units. Herein, we demonstrate an analog SiPho computing engine that relies on a coherent architecture and can perform optical TMM at the record-high speed of 50 GHz. Its potential to support DL applications, where the number of trainable parameters exceeds the available hardware dimensions, is highlighted through a photonic DNN that can reliably detect distributed denial-of-service attacks within a data center with a Cohen’s kappa score-based accuracy of 0.636.
Neuromorphic computing has emerged as a highly-promising compute alternative, migrating from von-Neuman architectures towards mimicking the human brain for sustaining computational power increases within a reduced power consumption envelope. Electronic neuromorphic chips like IBM’s TrueNorth, Intel’s Loihi and Mythic’s AI platform reveal a tremendous performance improvement in terms of computational speed and density; at the same time, neuromorphic photonic layouts are constantly gaining ground in exploiting their large component portfolio for enabling GHz-bandwidth and low-energy neurons. Progressing in tight synergy with appropriate training techniques, this evolution has already started to translate into performance improvements in end-to-end applications, highlighting the practical perspectives of the new neural network hardware when effectively synergized with new training frameworks. Herein, we present a complete portfolio of neuromorphic photonic subsystems and architectures, highlighting their utilization in practical application scenario for time series classification and fiber transmission links. Our work extends along feed-forward and recurrent photonic NN models, demonstrating experimental results together with the required training methods for bridging the gap between software-deployed NNs and the photonic hardware. We report on the experimentally validated performance of a 10GHz photonic time series classification engine, presenting also preliminary results on how photonic neurons can replace DSP modules in end-to-end fiber transmission schemes. The perspectives of these layouts to yield energy and area efficiency benefits are discussed through a detailed energy and area breakdown of neuromorphic photonic technologies, highlighting a promising roadmap when plasmo-photonic hardware is adopted.
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