Mapping Arbitrary Spiking Neural Networks to the RAVENS Neuroprocessor

In neuromorphic computing, a hardware implementation of a spiking neural network is used to provide improved speed and power efficiency over simulations of the networks on a traditional Von Neumann architecture. These hardware implementations employ bio-inspired architecture usually consisting of artificial neurons and synapses implemented in either analog, digital, or mixed-signal circuits. Since these hardware spiking neural networks are designed to support arbitrary networks under the constraints imposed by the available hardware resource, they have to be programmed by off-chip software with awareness of those constraints. The TENNLab research group at the University of Tennessee, Knoxville has recently developed the RAVENS neuroprocessor. A digital implementation of RAVENS designed to support a 64-neuron spiking neural network is being taped out. This thesis presents the work done to improve the software solution for mapping arbitrary spiking neural networks to the RAVENS neuroprocessor.