Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions

TL;DR

This paper reviews the fundamentals, recent advances, and future prospects of optical computing as a promising paradigm for accelerating deep neural networks, addressing current limitations of traditional hardware platforms.

Contribution

It provides a comprehensive overview of optical computing techniques, device engineering, architecture design, and hardware/software co-design tailored for DNN acceleration.

Findings

Optical computing can significantly speed up DNN workloads.

New approaches enhance optical device engineering and circuit design.

Co-design strategies improve performance and energy efficiency.

Abstract

Emerging artificial intelligence applications across the domains of computer vision, natural language processing, graph processing, and sequence prediction increasingly rely on deep neural networks (DNNs). These DNNs require significant compute and memory resources for training and inference. Traditional computing platforms such as CPUs, GPUs, and TPUs are struggling to keep up with the demands of the increasingly complex and diverse DNNs. Optical computing represents an exciting new paradigm for light-speed acceleration of DNN workloads. In this article, we discuss the fundamentals and state-of-the-art developments in optical computing, with an emphasis on DNN acceleration. Various promising approaches are described for engineering optical devices, enhancing optical circuits, and designing architectures that can adapt optical computing to a variety of DNN workloads. Novel techniques for hardware/software co-design that can intelligently tune and map DNN models to improve performance and energy-efficiency on optical computing platforms across high performance and resource constrained embedded, edge, and IoT platforms are also discussed. Lastly, several open problems and future directions for research in this domain are highlighted.