Reconfigurable communication fabric for efficient implementation of neural networks

Handling heavy multicast-based inter-neuron communication is the most challenging issue in parallel implementation of neural networks. To address this problem, a reconfigurable Network-on-Chip (NoC) architecture for neural networks is presented in this paper. The NoC consists of a number of node clusters with a fix topology connected by a reconfigurable inter-cluster communication fabric that efficiently handles multicast communication. The evaluation results show that the proposed architecture can better manage the multicast-based traffic of neural networks than the mesh-based topologies proposed in prior work. It offers up to 60% and 22% lower average message latency compared to a baseline and a state-of-the-art NoC for neural networks, respectively, which directly translates to faster neural processing.