Design of router for spiking neural networks

The development of large-scale networks with artificial neurons and adaptive synapses has suggested new avenues of exploration for brain-like cognitive computing. Spiking neural networks (SNNs), highly inspired from natural computing in the brain and recent advances in neurosciences, are often referred to as the 3
^th
generation of neural network, and become a new research hotspot in the era of artificial intelligence. SNN architecture supports simpler category of biologically-inspired neuron models and more complex large-scale interconnection in on-chip network of neurosynaptic cores. This paper introduces the design of router used in a spiking neural network, which is able to send and receive spiking information in network properly, as well as perfectly dealing with network anomaly such as data race or traffic congestion. This router is designed for the network at a scale of 64 * 64 neurosynaptic cores at the most, with 256 neurons in each cores. Both the area and estimated power consumption is acceptable. This router could also be applied to larger scale of SNN architecture networks effectively.