Spatial-Temporal Enhancement of ACO-Based Selection Schemes for Adaptive Routing in Network-on-Chip Systems

Networks-on-Chip (NoC) provides a regular and scalable design architecture for chip multi-processor (CMP) systems. The Ant Colony Optimization (ACO) is a distributed algorithm. Applying ACO to selection models of adaptive routing can improve NoC performance. Currently, ACO-based selection only uses the historical traffic information. While additional temporal and spatial information provides better approximation of network status for global load-balancing. In this paper, we first consider the temporal enhancement of congestion information. We propose the Multi-Pheromone ACO-based (MP-ACO) selection scheme which adopts the concept of Exponential Moving Average (EMA) from stock market. We implement a novel ACO system where ants lay two kinds of pheromones with different evaporation rates. The temporal pheromone variation can help to capture hidden-state dependencies of upcoming congestion status. Secondly, to acquire the spatial range of congestion information, we propose Regional-Aware ACO-based (RA-ACO) selection to record historical buffer information from routers within two-hop of distances, which helps to extend spatial pheromone coverage. Information provided by the proposed two schemes improves the system performance. Simulation results show that MP-ACO and RA-ACO with Odd-Even routing algorithm yields an improvement in saturation throughput over OBL and NoP selection by 14.38 percent and 18.64 percent, respectively. The router architectures for the proposed schemes are also implemented and analyze with small hardware overhead.