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.

翻译：深度学习已赋能多种物联网（IoT）应用。然而，设计兼具高精度与计算效率的模型仍是一项重大挑战，尤其在实时视频处理应用中。此类应用存在显著的帧间与帧内冗余，为进一步优化提供了可能。本文提出一种基于相似性感知的训练方法，利用视频帧中的数据冗余实现高效处理。我们的方法引入逐层正则化，通过提升权重的相似性来增强计算复用。我们在车道检测和场景解析两个关键实时应用上验证了该方法。观察到不同模型平均压缩比约为50%，加速比约~1.5倍，同时保持相同精度。