MASPINN: novel concepts for a neuroaccelerator for spiking neural networks

We present the basic architecture of a Memory Optimized Accelerator for Spiking Neural Networks. The accelerator architecture exploits two novel concepts for an efficient computation of spiking neural networks: weight caching and a compressed memory organization. These concepts allow a further parallelization in processing and reduce bandwidth requirements on accelerator's components. Therefore, they pave the way to dedicated digital hardware for real-time computation of more complex networks of pulse-coded neurons in the order of 10⁶ neurons. The programmable neuron model which the accelerator is based on is described extensively. This shall encourage a discussion and suggestions on features which would be desirable to add to the current model.