论文标题
边缘设备上超光视频智能的数据模型电路三设计
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices
论文作者
论文摘要
在本文中，我们为高通用，低成本和高智能多对象跟踪（MOT）提出了一个数据模型硬件三设计框架（HD）视频流。首先，为了启用超轻型视频智能，我们提出了时间框架过滤和空间显着性方法，以降低大量视频数据的复杂性。其次，我们利用结构感知的重量稀疏性来设计适合硬件友好的模型压缩方法。第三，在辅助数据和模型复杂性降低方面，我们提出了一种稀疏感，可扩展和低功率加速器设计，旨在以高能量效率提供实时性能。与现有作品不同，我们迈出了可靠的软件/硬件合作，以实现现实的MOT模型实现。与最先进的MOT基线相比，我们的三设计方法可以实现12.5倍的延迟降低，有效的帧速率提高20.9倍，功率下降5.83倍，而9.78倍提高了能效的提高，而无需大量准确性。
In this paper, we propose a data-model-hardware tri-design framework for high-throughput, low-cost, and high-accuracy multi-object tracking (MOT) on High-Definition (HD) video stream. First, to enable ultra-light video intelligence, we propose temporal frame-filtering and spatial saliency-focusing approaches to reduce the complexity of massive video data. Second, we exploit structure-aware weight sparsity to design a hardware-friendly model compression method. Third, assisted with data and model complexity reduction, we propose a sparsity-aware, scalable, and low-power accelerator design, aiming to deliver real-time performance with high energy efficiency. Different from existing works, we make a solid step towards the synergized software/hardware co-optimization for realistic MOT model implementation. Compared to the state-of-the-art MOT baseline, our tri-design approach can achieve 12.5x latency reduction, 20.9x effective frame rate improvement, 5.83x lower power, and 9.78x better energy efficiency, without much accuracy drop.
