End-to-end deep learning with neuromorphic photonics

Abstract

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.