Live Demonstration: Event-based Learning in Spiking Networks on Analog Hardware using JAX

Recent advances in machine learning with neural networks require an increasing energy budget. Neuromorphic hardware platforms, such as the BrainScaleS-2 (BSS-2) system, aim to offer accelerated energy-efficient computing solutions through analog computation. However, scalable training of spiking neural networks (SNNs) on an analog substrate remains challenging. Our jaxsnn software framework enables data-efficient event-based gradient computation, tailored for optimizing networks on hardware operating on event data. In this demo, we showcase jaxsnn by solving the SHD or Yin-Yang benchmark tasks live on the BSS-2 system.