Live demonstration: Handwritten digit recognition using spiking deep belief networks on SpiNNaker

We demonstrate an interactive handwritten digit recognition system with a spike-based deep belief network running in real-time on SpiNNaker, a biologically inspired many-core architecture. Results show that during the simulation a SpiNNaker chip can deliver spikes in under 1 μs, with a classification latency in the order of tens of milliseconds, while consuming less than 0.3 W.