Methods and Analysis of Accelerating Edge Computing with Photonic Neural Networks in the Context of Autonomous Driving

Abstract

This paper reviews the latest research progress and key technologies in accelerating edge computing with optical neural networks, set against the backdrop of autonomous driving. It first outlines the context and research significance of autonomous driving, highlighting the technical limitations faced by traditional electronic computing. Subsequently, it provides a detailed analysis of the latest research achievements in optical neural networks and accelerated computing both domestically and internationally. This includes innovative applications of optical flow estimation methods (FocusFlow, CSFlow), introductions to methods for edge computing and autonomous driving collaboration (OVEAP, DVEAP), and innovative approaches to underlying computations (optical convolution, Monet channels). Through comparative analysis, it reveals current re-search hotspots and trends, analyzes existing short-comings and potential solutions, and explores possible future development directions. This paper aims to systematically explore how optical computing technology can overcome the limitations of traditional electronic computing to build a new generation of high-performance, low-power intelligent computing systems.