# A Reinforcement Learning Framework with Region-Awareness and Shared Path Experience for Efficient Routing in Networks-on-Chip

arXiv: https://arxiv.org/abs/2307.11712 · HTML: https://mlsanity.com/paper/2307.11712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2023.

## Built on by (most cited first, 1 of 1)

- [CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2410.07426/) (2024)

## Abstract (arXiv)

Network-on-chip (NoC) architectures provide a scalable, high-performance, and reliable interconnect for emerging manycore systems. The routing policies used in NoCs have a significant impact on overall performance. Prior efforts have proposed reinforcement learning (RL)-based adaptive routing policies to avoid congestion and minimize latency in NoCs. The output quality of RL policies depends on selecting a representative cost function and an effective update mechanism. Unfortunately, existing RL policies for NoC routing fail to represent path contention and regional congestion in the cost function. Moreover, the experience of packet flows sharing the same route is not fully incorporated into the RL update mechanism. In this paper, we present a novel regional congestion-aware RL-based NoC routing policy called Q-RASP that is capable of sharing experience from packets using the same routes. Q-RASP improves average packet latency by up to 18.3% and reduces NoC energy consumption by up to 6.7% with minimal area overheads compared to state-of-the-art RL-based NoC routing implementations.