ConvNets experiments on SpiNNaker

Abstract

The SpiNNaker Hardware platform allows emulating generic neural network topologies, where each neuron-to-neuron connection is defined by an independent synaptic weight. Consequently, storage requires an important amount of memory in the case of generic neural network topologies. This is solved in SpiNNaker by encapsulating with each SpiNNaker chip (which includes 18 ARM cores) a 128MB DRAM chip within the same package. However, ConvNets (Convolutional Neural Network) posses weight sharing property, so that many neuron-to-neuron connections share the same value. Therefore, a very reduced amount of memory is required to define all synaptic weights, which can be stored on local SRAM DTCM (data-tightly-coupled-memory) at each ARM core. This way, DRAM can be used extensively to store traffic data for off-line analyses. We show an implementation of a 5-layer ConvNet for symbol recognition. Symbols are obtained with a DVS camera. Neurons in the ConvNet operate in an event-driven fashion, and synapses operate instantly. With this approach it was possible to allocate up to 2048 neurons per ARM core, or equivalently 32k neurons per SpiNNaker chip.