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Radar sensors offer power-efficient solutions for always-on smart devices,but processing the data streams on resource-constrained embedded platformsremains challenging. This paper presents novel techniques that leverage thetemporal correlation present in streaming radar data to enhance the efficiencyof Early Exit Neural Networks for Deep Learning inference on embedded devices.These networks add additional classifier branches between the architecture'shidden layers that allow for an early termination of the inference if theirresult is deemed sufficient enough by an at-runtime decision mechanism. Ourmethods 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 perinference over a Single Exit Network and 12% over a confidence-based Early Exitversion. Our proposed techniques work on commodity hardware and can be combinedwith traditional optimizations, making them accessible for resource-constrainedembedded platforms commonly used in smart devices. Such efficiency gains enablereal-time radar data processing on resource-constrained platforms, allowing fornew applications in the context of smart homes, Internet-of-Things, andhuman-computer interaction.

Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing