Radar sensors offer power-efficient solutions for always-on smart devices, but processing the data streams on resource-constrained embedded platforms remains challenging. This paper presents novel techniques that leverage the temporal correlation present in streaming radar data to enhance the efficiency of Early Exit Neural Networks for Deep Learning inference on embedded devices. These networks add additional classifier branches between the architecture's hidden layers that allow for an early termination of the inference if their result is deemed sufficient enough by an at-runtime decision mechanism. Our methods enable more informed decisions on when to terminate the inference, reducing computational costs while maintaining a minimal loss of accuracy. Our results demonstrate that our techniques save up to 26% of operations per inference over a Single Exit Network and 12% over a confidence-based Early Exit version. Our proposed techniques work on commodity hardware and can be combined with traditional optimizations, making them accessible for resource-constrained embedded platforms commonly used in smart devices. Such efficiency gains enable real-time radar data processing on resource-constrained platforms, allowing for new applications in the context of smart homes, Internet-of-Things, and human-computer interaction.

翻译：雷达传感器为常开型智能设备提供了低功耗解决方案，但在资源受限的嵌入式平台上处理数据流仍具挑战。本文提出新颖技术，利用流式雷达数据中存在的时序相关性，提升嵌入式设备上早期退出神经网络用于深度学习推理的效率。该类网络在架构隐藏层之间添加额外分类分支，若运行时决策机制判定其结果足够可靠，则可提前终止推理进程。我们的方法能更明智地决策何时终止推理，在保持最小精度损失的同时降低计算成本。实验结果表明，与单退出网络相比，我们的技术每个推理可节省高达26%的计算操作；与基于置信度的早期退出版本相比，可节省12%。所提技术适用于商用硬件，并能与传统优化方法结合，使通常用于智能设备的资源受限嵌入式平台能够轻松部署。这种效率提升使资源受限平台能够实现实时雷达数据处理，从而为智能家居、物联网及人机交互等领域催生新型应用。