Optical Flow Estimation using SPECK™ Neuromorphic Hardware

Abstract

Neuromorphic hardware and spiking neural networks (SNNs) offer a bio-inspired path to low-latency, energy-efficient computation by emulating the brain’s asynchronous spike-based processing, particularly attractive for real-time optical flow estimation on resource-constrained micro aerial vehicles (MAVs). We leverage a SynSense SPECK™ system-on-chip, which integrates a Dynamic Vision Sensor (DVS) with a neuromorphic processor, to realize live, onboard flow-based attitude and thrust control from dense event-based optical flow. Our inference architecture combines spiking and analog layers in a hybrid SNN-ANN framework, enabling the use of SPECK™ for regression task in a closed-loop drone control, an application not previously demonstrated on the chip. Despite the chip’s compact form factor, the system produces dense flow in real time and achieves stable indoor hover using flow-based control. The hybrid pipeline runs ~2× faster than an ANN-only baseline at identical power. These results highlight the promise of neuromorphic sensing and processing for ultra-efficient, autonomous flight in real-world scenarios.