  Spiking neural networks (SNNs) for event-based optical flow are claimed to be
computationally more efficient than their artificial neural networks (ANNs)
counterparts, but a fair comparison is missing in the literature. In this work,
we propose an event-based optical flow solution based on activation
sparsification and a neuromorphic processor, SENECA. SENECA has an event-driven
processing mechanism that can exploit the sparsity in ANN activations and SNN
spikes to accelerate the inference of both types of neural networks. The ANN
and the SNN for comparison have similar low activation/spike density (~5%)
thanks to our novel sparsification-aware training. In the hardware-in-loop
experiments designed to deduce the average time and energy consumption, the SNN
consumes 44.9ms and 927.0 microjoules, which are 62.5% and 75.2% of the ANN's
consumption, respectively. We find that SNN's higher efficiency attributes to
its lower pixel-wise spike density (43.5% vs. 66.5%) that requires fewer memory
access operations for neuron states.
