Best of Both Worlds: Hybrid SNN-ANN Architecture for Event-based Optical Flow Estimation

Event-based cameras offer a low-power alternative to frame-based cameras for capturing high-speed motion and high dynamic range scenes. They provide asynchronous streams of sparse events. 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 (ANNs1) fail to process event data effectively. However, training SNNs is difficult due to additional trainable parameters (thresholds and leaks), vanishing spikes at deeper layers, non-differentiable binary activation function etc. Moreover, 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, 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. While the ANN layers offer trouble-free training and implementation on standard machine learning hardware such as GPUs. We provide extensive experimental analysis for assigning each layer to be spiking or analog in nature, leading to a network configuration optimized for performance and ease of training. We evaluate our hybrid architectures for optical flow estimation using event-data on DSEC-flow and Mutli-Vehicle Stereo Event-Camera (MVSEC) datasets. The results indicate that our configured hybrid architectures outperform the state-of-the-art ANN-only, SNN-only and past hybrid architectures both in terms of accuracy and efficiency. Specifically, our hybrid architecture exhibit a 31% and 24.8% lower average endpoint error (AEE) at 2.1x and 3.1x lower energy, compared to an SNN-only architecture on DSEC and MVSEC datasets, respectively.