ACO-based Cascaded Adaptive Routing for traffic balancing in NoC systems

Ant Colony Optimization (ACO) is a bio-inspired algorithm extensively applied in optimization problems. The performance of Network-on-Chip (NoC) is generally dominated by traffic distribution and routing. With more precise network information for path selection by using pheromone, ACO-based adaptive routing has higher potential to overcome the unbalance and unpredictable traffic load. On the other hand, the implementation cost of ACO is in general too high to store network information in pheromone memory, which is a routing table of all destination-channel pairs. We propose an ACO-based Cascaded Adaptive Routing (ACO-CAR) by combining two features: 1) table reforming by eliminating redundant information of far destinations from full routing table, and 2) adaptive searching of cascaded point for more precise network information. Our experimental results show that ACO-CAR has lower latency and higher saturation throughput, and can be implemented with 19.05% memory of full routing table.