A Brain-Inspired Network-on-Chip Architecture with Hybrid-Mode Routing

The rise and development of large-scale neuromorphic platforms require network-on-chip to support efficient data transmission mechanisms. Although many efforts have been made to develop high performance topology architectures and routing schemes, they still suffer from single transmission mode or poor scalability, making them stay on a low efficiency in neuromorphic computing. Inspired by the small-world properties of human brain networks, this brief proposes an efficient region-broadcast (ReB) routing scheme to support unicast, multicast, and broadcast transmission modes. Besides, a synaptic connections indexing method is deployed to accommodate the region-broadcast routing scheme and support this hybrid-mode packet transmission. This method replaces the traditional multicast routing table, effectively improving network scalability and reducing power consumption. Experimental results show that compared to existing work, the ReB routing scheme reduces the peak spike traffic and link load standard deviation by 11.5% and 20.4%, respectively. The ReB scheme brings improvements in latency, throughput, and energy under the validation of synthetic traffic, spiking neural network applications and brain cortical networks. Various synthetic traffic patterns are used in the experiments. The datasets used in spiking neural network applications include MNIST, QTDB, Ev-object, and DVS-Gesture. Finally, the proposed ReB router has an excellent bandwidth of 0.24 spike/cycle and only consumes an area of 0.014 mm².