Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions
This work tackles the problem of high compute and memory demands for DNNs in AI applications, presenting a review of optical computing advancements as a potential solution.
The paper addresses the challenge of accelerating deep neural networks (DNNs) by exploring optical computing as a new paradigm for light-speed performance and energy efficiency, discussing various approaches and co-design techniques without providing specific numerical results.
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.