Accelerated Computer Vision Inference with AI on the Edge

Abstract

Computer vision is not just about breaking down images or videos into constituent pixels, but also about making sense of those pixels and comprehending what they represent. Researchers have developed some brilliant neural networks and algorithms for modern computer vision. Tremendous developments have been observed in deep learning as computational power is getting cheaper. But data-driven deep learning and cloud computing based systems face some serious limitations at edge devices in real-world scenarios. Since we cannot bring edge devices to the data-centers, so we bring AI to the edge devices with AI on the Edge. OpenVINO toolkit is a powerful tool that facilitates deployment of high-performance computer vision applications to the edge devices. It converts existing applications into hardwarefriendly and inference-optimized deployable runtime packages that operate seamlessly at the edge. The goals of this paper are to describe an in-depth survey of problems faced in existing computer vision applications and to present AI on the Edge along with OpenVINO toolkit as the solution to those problems. We redefine the workflow for deploying computer vision systems and provide an efficient approach for development and deployment of edge applications. Furthermore, we summarize the possible works and applications of AI on the Edge in future in regard to security and privacy.