Evaluating Spiking Neural Network On Neuromorphic Platform For Human
  Activity Recognition
Sizhen Bian Michele Magno
Energy efficiency and low latency are crucial requirements for designing
wearable AI-empowered human activity recognition systems, due to the hard
constraints of battery operations and closed-loop feedback. While neural
network models have been extensively compressed to match the stringent edge
requirements, spiking neural networks and event-based sensing are recently
emerging as promising solutions to further improve performance due to their
inherent energy efficiency and capacity to process spatiotemporal data in very
low latency. This work aims to evaluate the effectiveness of spiking neural
networks on neuromorphic processors in human activity recognition for wearable
applications. The case of workout recognition with wrist-worn wearable motion
sensors is used as a study. A multi-threshold delta modulation approach is
utilized for encoding the input sensor data into spike trains to move the
pipeline into the event-based approach. The spikes trains are then fed to a
spiking neural network with direct-event training, and the trained model is
deployed on the research neuromorphic platform from Intel, Loihi, to evaluate
energy and latency efficiency. Test results show that the spike-based workouts
recognition system can achieve a comparable accuracy (87.5\%) comparable to the
popular milliwatt RISC-V bases multi-core processor GAP8 with a traditional
neural network ( 88.1\%) while achieving two times better energy-delay product
(0.66 \si{\micro\joule\second} vs. 1.32 \si{\micro\joule\second}).