# RLPlanner: Reinforcement Learning based Floorplanning for Chiplets with Fast Thermal Analysis

arXiv: https://arxiv.org/abs/2312.16895 · HTML: https://mlsanity.com/paper/2312.16895/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks](https://mlsanity.com/paper/2009.09009/) (2020)

## Built on by (most cited first, 2 of 2)

- [Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation](https://mlsanity.com/paper/2404.04943/) (2024)
- [RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning](https://mlsanity.com/paper/2601.22476/) (2026)

## Abstract (arXiv)

Chiplet-based systems have gained significant attention in recent years due to their low cost and competitive performance. As the complexity and compactness of a chiplet-based system increase, careful consideration must be given to microbump assignments, interconnect delays, and thermal limitations during the floorplanning stage. This paper introduces RLPlanner, an efficient early-stage floorplanning tool for chiplet-based systems with a novel fast thermal evaluation method. RLPlanner employs advanced reinforcement learning to jointly minimize total wirelength and temperature. To alleviate the time-consuming thermal calculations, RLPlanner incorporates the developed fast thermal evaluation method to expedite the iterations and optimizations. Comprehensive experiments demonstrate that our proposed fast thermal evaluation method achieves a mean absolute error (MAE) of 0.25 K and delivers over 120x speed-up compared to the open-source thermal solver HotSpot. When integrated with our fast thermal evaluation method, RLPlanner achieves an average improvement of 20.28% in minimizing the target objective (a combination of wirelength and temperature), within a similar running time, compared to the classic simulated annealing method with HotSpot.