PKCA: A priori-knowledge and congestion-awareness method for adaptive routing algorithms in mesh architectures

Adaptive routing algorithms have been widely utilized in Network-on-Chip (NoC) architectures and have shown to enhance overall throughput in numerous studies. The adaptive routing algorithms can effectively detect network congestion. On the one hand, one-hop awareness or local awareness can easily detect network congestion but may also result in local greed. On the other hand, global awareness is better for load balancing, but it is difficult to be aware of the network congestion status. This article proposes a lightweight adaptive on-chip routing algorithm based on the concentric circles theory and prior knowledge derived from real-life observations. The algorithm, named the Priori-Knowledge and Congestion-Awareness method (PKCA), aims to optimize the routing efficiency within the chip. PKCA is designed to be not only simple but also to have low time complexity, allowing it to calculate paths to destinations without local greed. We performed evaluations, and the results demonstrated that our design surpasses one-hop awareness, two-hop awareness, and global awareness by 31%, 25%, and 20%, respectively, in terms of latency, and by 22%, 14%, and 22%, respectively, in terms of throughput. Furthermore, the time complexity is only O(N)$$ O(N) $$ in an NxN$$ N\times N $$ 2D mesh. This article proposes a lightweight adaptive on-chip routing algorithm NoC based on the concentric circles theory and prior knowledge derived from real-life observations. The algorithm, named the Priori-Knowledge and Congestion-Awareness method (PKCA), aims to optimize the routing efficiency within the chip. image