In recent years, deep neural networks (DNNs) have gained widespread adoption for continuous mobile object detection (OD) tasks, particularly in autonomous systems. However, a prevalent issue in their deployment is the one-size-fits-all approach, where a single DNN is used, resulting in inefficient utilization of computational resources. This inefficiency is particularly detrimental in energy-constrained systems, as it degrades overall system efficiency. We identify that, the contextual information embedded in the input data stream (e.g. the frames in the camera feed that the OD models are run on) could be exploited to allow a more efficient multi-model-based OD process. In this paper, we propose SHIFT which continuously selects from a variety of DNN-based OD models depending on the dynamically changing contextual information and computational constraints. During this selection, SHIFT uniquely considers multi-accelerator execution to better optimize the energy-efficiency while satisfying the latency constraints. Our proposed methodology results in improvements of up to 7.5x in energy usage and 2.8x in latency compared to state-of-the-art GPU-based single model OD approaches.

翻译：近年来，深度神经网络（DNN）在连续移动目标检测任务中得到了广泛应用，尤其是在自主系统中。然而，其部署中的一个普遍问题是采用“一刀切”的方法，仅使用单一DNN，导致计算资源利用效率低下。这种低效性在能源受限的系统中尤为不利，因为它会降低整体系统效率。我们发现，输入数据流中嵌入的上下文信息（例如，用于运行目标检测模型的摄像头视频帧）可以被利用来实现更高效的多模型目标检测流程。在本文中，我们提出了SHIFT方法，该方法根据动态变化的上下文信息和计算约束，从多种基于DNN的目标检测模型中持续进行选择。在此选择过程中，SHIFT独特地考虑了多加速器执行，以更好地优化能效，同时满足延迟约束。与最先进的基于GPU的单一模型目标检测方法相比，我们提出的方法在能耗方面提升了高达7.5倍，在延迟方面提升了2.8倍。