Power analysis of large-scale, real-time neural networks on SpiNNaker

Stromatias, Evangelos and Galluppi, Francesco and Cameron Patterson and Furber, Steve

Abstract

Simulating large spiking neural networks is non trivial: supercomputers offer great flexibility at the price of power and communication overheads; custom neuromorphic circuits are more power efficient but less flexible; while alternative approaches based on GPGPUs and FPGAs, whilst being more readily available, show similar model specialization. As well as efficiency and flexibility, real time simulation is a desirable neural network characteristic, for example in cognitive robotics where embodied agents interact with the environment using low-power, event-based neuromorphic sensors. The SpiNNaker neuromimetic architecture has been designed to address these requirements, simulating large-scale heterogeneous models of spiking neurons in real-time, offering a unique combination of flexibility, scalability and power efficiency. In this work a 48-chip board is utilised to generate a SpiNNaker power estimation model, based on numbers of neurons, synapses and their firing rates. In addition, we demonstrate simulations capable of handling up to a quarter of a million neurons, 81 million synapses and 1.8 billion synaptic events per second, with the most complex simulations consuming less than 1 Watt per SpiNNaker chip.