  Optical flow provides information on relative motion that is an important
component in many computer vision pipelines. Neural networks provide high
accuracy optical flow, yet their complexity is often prohibitive for
application at the edge or in robots, where efficiency and latency play crucial
role. To address this challenge, we build on the latest developments in
event-based vision and spiking neural networks. We propose a new network
architecture, inspired by Timelens, that improves the state-of-the-art
self-supervised optical flow accuracy when operated both in spiking and
non-spiking mode. To implement a real-time pipeline with a physical event
camera, we propose a methodology for principled model simplification based on
activity and latency analysis. We demonstrate high speed optical flow
prediction with almost two orders of magnitude reduced complexity while
maintaining the accuracy, opening the path for real-time deployments.
