  Spiking Neural Networks (SNN) are a class of bio-inspired neural networks
that promise to bring low-power and low-latency inference to edge devices
through asynchronous and sparse processing. However, being temporal models,
SNNs depend heavily on expressive states to generate predictions on par with
classical artificial neural networks (ANNs). These states converge only after
long transient periods, and quickly decay without input data, leading to higher
latency, power consumption, and lower accuracy. This work addresses this issue
by initializing the state with an auxiliary ANN running at a low rate. The SNN
then uses the state to generate predictions with high temporal resolution until
the next initialization phase. Our hybrid ANN-SNN model thus combines the best
of both worlds: It does not suffer from long state transients and state decay
thanks to the ANN, and can generate predictions with high temporal resolution,
low latency, and low power thanks to the SNN. We show for the task of
event-based 2D and 3D human pose estimation that our method consumes 88% less
power with only a 4% decrease in performance compared to its fully ANN
counterparts when run at the same inference rate. Moreover, when compared to
SNNs, our method achieves a 74% lower error. This research thus provides a new
understanding of how ANNs and SNNs can be used to maximize their respective
benefits.
