Vega: A Computer Vision Processing Enhancement Framework with Graph-based Acceleration

Abstract

The popularity of Computer Vision (CV) algorithms has been on the rise given their growing dependence on machine learning and deep neural networks. The resulting improvement in inference accuracy has revolutionized a number of fields. However, given that CV algorithms consist of many different stages, each having different computing characteristics, their execution is frequently irregular and inefficient, unable to leverage the full potential of the computing platform. Presently, supporting real-time video processing for high resolution images on edge systems involves a significant amount of programming effort and performance tuning. To overcome this challenge, we present Vega, a parallel graph-based framework that enables better utilization of multi-core edge computing platforms. Vega provides a highly flexible and user-friendly interface to execute a range of CV algorithms efficiently, leveraging multiple external libraries for performance. First, Vega maps independent stages of a CV algorithm to nodes in a pipeline graph. Next, it dynamically schedules nodes on a multi-core CPU using multi-threading. From our experimental results, our framework improves performance of all selected algorithms by at least 1.75x and up to 4.82x on the same platform. We analyze the impact of using our framework in terms of hardware utilization, frame processing latency and throughput.