Generating Brain-Network-Inspired Topologies for Large-Scale NoCs on Monolithic 3D ICs

Abstract

Network-on-chips (NoCs) with traditional regular topologies generally have an average-hop-count increased, polynomially, with the network size. This brief proposes to generate brain-network-inspired (BNI) topologies on monolithic 3D (M3D) ICs, where the average hop count scales up logarithmically, for large-scale systems with tens of thousands of cores. We propose a routability-driven method to generate BNI topologies on M3D ICs with scale-free and small-world properties. Moreover, we present an iterative method to reduce the network diameter using as few tier-crossing routers as possible. The resulting BNI topologies not only have over 50% lower average hop count and lower diameter than traditional regular topologies, but also have shorter total link length than other irregular topologies. The performance is further validated by the cycle-accurate simulation.