Quantifying power use in silicon photonic neural networks

Abstract

Due to challenging efficiency limits facing conventional and unconventional electronic architectures, information processors based on photonics have attracted renewed interest. Research communities have yet to settle on definitive techniques to describe the performance of this class of information processors. Photonic systems are different from electronic ones, so the existing concepts of computer performance measurement cannot necessarily apply. In this manuscript, we attempt to quantify the power use of photonic neural networks with state-of-the-art and future hardware. We derive scaling laws, physical limits, and new platform performance metrics. We find that overall performance is regime-like, which means that energy efficiency characteristics of a photonic processor can be completely described by no less than seven performance numbers. The introduction of these analytical strategies provides a much needed foundation for quantitative roadmapping and commercial value assignment for silicon photonic neural networks.

9 figures

References in corpus (6)

• A BaTiO3-Based Electro-Optic Pockels Modulator Monolithically Integrated on an Advanced Silicon Photonics Platform
• WRPN: Wide Reduced-Precision Networks
• MEMS for Photonic Integrated Circuits
• Post-Fabrication Trimming of Silicon Photonic Ring Resonators at Wafer-Scale
• MorphoNoC: Exploring the Design Space of a Configurable Hybrid NoC using Nanophotonics
• Single photonic perceptron based on a soliton crystal Kerr microcomb for high-speed, scalable, optical neural networks

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