We make the first attempt to propose a novel and comprehensive reinforcement learning framework for adaptive routing on NoCs, called RELAR.

We make the first attempt to propose a novel and comprehensive reinforcement learning framework for adaptive routing on NoCs, called RELAR.

Routing adaptiveness is one of the most important metrics of adaptive routing algorithms, which is defined as the ratio of the number of allowed minimal paths.

This paper proposes a novel deep reinforcement learning framework for adaptive routing called DRLAR that is suitable for diversified traffic patterns.

RELAR is suitable for diversified traffic patterns and resolve multi-objective optimization simultaneously. We conduct experiments against state-of-the-art ...

Jul 21, 2023 · 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 ...

Dec 15, 2023 · This paper proposes a novel deep reinforcement learning framework for adaptive routing called DRLAR that is suitable for diversified traffic ...

Jun 1, 2024 · This paper proposes a novel deep reinforcement learning framework for adaptive routing called DRLAR that is suitable for diversified traffic ...

Jul 17, 2024 · This paper proposes a novel deep reinforcement learning framework for adaptive routing called DRLAR that is suitable for diversified traffic ...

RELAR: A Reinforcement Learning Framework for Adaptive Routing in Network-on-Chips ... We design a novel low-cost congestion propagation network that ...