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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

proceedings of the ... annual hawaii international conference on system sciences/proceedings of the annual hawaii international conference on system sciences

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