Zero-Load Predictive Model for Performance Analysis in Deflection Routing {NoCs}

Abstract

We study a static model for 2-D and 3-D networks that accurately represents the average distance travelled by packets under deflection routing, which is a specific form of adaptive routing. The model captures static properties of the network topology and the spatial distribution of traffic, but does not take into account traffic loading and congestion. Even though this static model cannot accurately predict packet latency under high load, we contend that it is a perfect predictor of deflection routing networks' relative performance under any load condition below saturation, and thus always correctly predicts the optimum network configuration. This is verified through cycle-accurate simulations of congested and uncongested networks with fully adaptive, deflection routing for regular traffic patterns such as uniform random, localised, bursty, and others, as well as irregular patterns in both regular and irregular networks. As the networks with minimal average distance perform best even under high traffic load, the average distance model establishes a robust relation between a static network property, average distance, and network performance under load, providing new insight into network behaviour and an opportunity to identify the optimal network configuration without time-consuming simulations.