Spiking Cooperative Stereo-Matching at 2 ms Latency with Neuromorphic Hardware.

We demonstrate a spiking neural network that extracts spatial depth information from a stereoscopic visual input stream. The system makes use of a scalable neuromorphic computing platform, SpiNNaker, and neuromorphic vision sensors, so called silicon retinas, to solve the stereo matching (correspondence) problem in real-time. It dynamically fuses two retinal event streams into a depth-resolved event stream with a fixed latency of 2 ms, even at input rates as high as several 100,000 events per second. The network design is simple and portable so it can run on many types of neuromorphic computing platforms including FPGAs and dedicated silicon.