Treasure What You Have: Exploiting Similarity in Deep Neural Networks
  for Efficient Video Processing
Hadjer Benmeziane Halima Bouzidi Hamza Ouarnoughi Ozcan Ozturk Smail Niar
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.