Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing

• URL: http://arxiv.org/abs/2305.06492v1
• Date: Wed, 10 May 2023 23:18:47 GMT
• Title: Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing
• Authors: Hadjer Benmeziane, Halima Bouzidi, Hamza Ouarnoughi, Ozcan Ozturk and Smail Niar
• Abstract summary: This paper proposes a similarity-aware training methodology that exploits data redundancy in video frames for efficient processing. We validate our methodology on two critical real-time applications, lane detection and scene parsing.
• Score: 1.5749416770494706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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