Markov Chain Monte Carlo inference on graphical models using event-based processing on the SpiNNaker neuromorphic architecture

We present a combined hardware/software architecture to perform Markov Chain Monte Carlo sampling on probabilistic graphical models in a brain-inspired, energy-aware manner. By combining massively-parallel neuromorphic hardware architecture (SpiNNaker) with algorithms we´ve have developed for the event-based framework employed in SpiNNaker, we achieve large speedups when performing inference as compared to a traditional PC. We present results from two sampling approaches both well suited to the SpiNNaker architecture. Neural sampling, the first of the two approaches relies directly on simulating networks of spiking neurons while the second, Gibb´s sampling is more flexible but still takes advantage of the hardware´s event-handling capabilities.