Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar
  Data Processing
Max Sponner Julius Ott Lorenzo Servadei Bernd Waschneck Robert Wille Akash Kumar
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.