  Event-based cameras have recently shown great potential for high-speed motion
estimation owing to their ability to capture temporally rich information
asynchronously. Spiking Neural Networks (SNNs), with their neuro-inspired
event-driven processing can efficiently handle such asynchronous data, while
neuron models such as the leaky-integrate and fire (LIF) can keep track of the
quintessential timing information contained in the inputs. SNNs achieve this by
maintaining a dynamic state in the neuron memory, retaining important
information while forgetting redundant data over time. Thus, we posit that SNNs
would allow for better performance on sequential regression tasks compared to
similarly sized Analog Neural Networks (ANNs). However, deep SNNs are difficult
to train due to vanishing spikes at later layers. To that effect, we propose an
adaptive fully-spiking framework with learnable neuronal dynamics to alleviate
the spike vanishing problem. We utilize surrogate gradient-based
backpropagation through time (BPTT) to train our deep SNNs from scratch. We
validate our approach for the task of optical flow estimation on the
Multi-Vehicle Stereo Event-Camera (MVSEC) dataset and the DSEC-Flow dataset.
Our experiments on these datasets show an average reduction of 13% in average
endpoint error (AEE) compared to state-of-the-art ANNs. We also explore several
down-scaled models and observe that our SNN models consistently outperform
similarly sized ANNs offering 10%-16% lower AEE. These results demonstrate the
importance of SNNs for smaller models and their suitability at the edge. In
terms of efficiency, our SNNs offer substantial savings in network parameters
(48.3x) and computational energy (10.2x) while attaining ~10% lower EPE
compared to the state-of-the-art ANN implementations.
