Regional ACO-based routing for load-balancing in NoC systems

Ant Colony Optimization (ACO) is a problem-solving technique that was inspired by the related research on the behavior of real-world ant colony. In the domain of Network-on-chip (NoC), ACO-based adaptive routing has been applied to achieve load-balancing effectively with historical information. However, the cost of the ACO network pheromone table is too high, and this overhead grows fast with the scaling of NoC. In order to fix this problem, it is essential to model the ACO algorithm in more careful consideration of the system architecture, available hardware resource, and appropriate transformation from the ant colony metaphor. In this paper, we analyzed the NoC network characteristic and bring about the corresponding issues of implementing ACO on NoC. We proposed a Regional ACO-based routing (RACO) with static and dynamic regional table forming technique to reduce the cost of table, share pheromone information, and adopt look-ahead model for further load-balancing. The experimental results show that RACO can be implemented with less memory, less cost increase on scaling, and better performance of load-balancing compared to traditional ACO-based routing.