  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.
