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.

SHIFT 方法通过利用输入数据流中的上下文信息和计算约束，连续选择多种基于深度神经网络的物体检测模型，从而实现更高的能源利用效率和低延迟。相比先进的基于 GPU 的单模型物体检测方法，在能源使用上提升了 7.5 倍，在延迟上提升了 2.8 倍。