Multi-Pheromone ACO-based routing in Network-on-Chip system inspired by economic phenomenon

Ant Colony Optimization (ACO) is a collective intelligence problem-solving paradigm. By ACO, we can effectively distribute the central control unit to achieve higher performance. With the scaling of Network-on-Chip (NoC) size, more complex communication problems can severely harm the system performance. Therefore, we need more efficient ACO-adaptive routing to achieve better trend prediction for global load-balancing. In this paper, we introduce a Multi-Pheromone ACO-based (MPACO) routing to make better use of the network information and provide a deeper look to the local model. By adopting the concept of Exponential Moving Average (EMA) in stock market, MPACO provide additional dimension aspect: rate of change in network information by laying pheromone with different evaporation speed. The experimental results show that MPACO can achieve higher performance while maintaining similar implementation cost compared to the previous work.