Deep learning has enabled various Internet of Things (IoT) applications. Still, designing models with high accuracy and computational efficiency remains a significant challenge, especially in real-time video processing applications. Such applications exhibit high inter- and intra-frame redundancy, allowing further improvement. This paper proposes a similarity-aware training methodology that exploits data redundancy in video frames for efficient processing. Our approach introduces a per-layer regularization that enhances computation reuse by increasing the similarity of weights during training. We validate our methodology on two critical real-time applications, lane detection and scene parsing. We observe an average compression ratio of approximately 50% and a speedup of \sim 1.5x for different models while maintaining the same accuracy.
@article{arxiv.2305.06492,
title = {Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing},
author = {Hadjer Benmeziane and Halima Bouzidi and Hamza Ouarnoughi and Ozcan Ozturk and Smail Niar},
journal= {arXiv preprint arXiv:2305.06492},
year = {2023}
}