  In the field of robotics, event-based cameras are emerging as a promising
low-power alternative to traditional frame-based cameras for capturing
high-speed motion and high dynamic range scenes. This is due to their sparse
and asynchronous event outputs. Spiking Neural Networks (SNNs) with their
asynchronous event-driven compute, show great potential for extracting the
spatio-temporal features from these event streams. In contrast, the standard
Analog Neural Networks (ANNs) fail to process event data effectively. However,
training SNNs is difficult due to additional trainable parameters (thresholds
and leaks), vanishing spikes at deeper layers, and a non-differentiable binary
activation function. Furthermore, an additional data structure, membrane
potential, responsible for keeping track of temporal information, must be
fetched and updated at every timestep in SNNs. To overcome these challenges, we
propose a novel SNN-ANN hybrid architecture that combines the strengths of
both. Specifically, we leverage the asynchronous compute capabilities of SNN
layers to effectively extract the input temporal information. Concurrently, the
ANN layers facilitate training and efficient hardware deployment on traditional
machine learning hardware such as GPUs. We provide extensive experimental
analysis for assigning each layer to be spiking or analog, leading to a network
configuration optimized for performance and ease of training. We evaluate our
hybrid architecture for optical flow estimation on DSEC-flow and Multi-Vehicle
Stereo Event-Camera (MVSEC) datasets. On the DSEC-flow dataset, the hybrid
SNN-ANN architecture achieves a 40% reduction in average endpoint error (AEE)
with 22% lower energy consumption compared to Full-SNN, and 48% lower AEE
compared to Full-ANN, while maintaining comparable energy usage.
