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.
Abstract (translated)
深度学习已经使各种物联网(IoT)应用得以实现。然而，设计高精度和高计算效率的模型仍然是一个 significant 挑战，特别是实时视频处理应用中。这些应用表现出内外帧的冗余，从而允许进一步改进。本文提出了一种相似性 aware 的训练方法，利用视频帧中的数据冗余以高效处理。我们的方法引入了每层 Regularization，在训练期间通过增加权重之间的相似性来提高计算重用。我们对两个关键实时应用，车道检测和场景解析进行了验证。我们观察到平均压缩比例约为 50%，不同模型的速度up 达到了 \sim 1.5x，同时保持相同的精度。
URL
https://arxiv.org/abs/2305.06492
