# Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing

arXiv: https://arxiv.org/abs/2011.07577 · HTML: https://mlsanity.com/paper/2011.07577/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [On Joint Learning for Solving Placement and Routing in Chip Design](https://mlsanity.com/paper/2111.00234/) (2021)
- [MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning](https://mlsanity.com/paper/2211.13382/) (2022)
- [ChiPFormer: Transferable Chip Placement via Offline Decision Transformer](https://mlsanity.com/paper/2306.14744/) (2023)
- [Macro Placement by Wire-Mask-Guided Black-Box Optimization](https://mlsanity.com/paper/2306.16844/) (2023)
- [Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithms](https://mlsanity.com/paper/2407.15026/) (2024)
- [Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer](https://mlsanity.com/paper/2412.07167/) (2024)
- [BBOPlace-Bench: Benchmarking Black-Box Optimization for Chip Placement](https://mlsanity.com/paper/2510.23472/) (2025)
- [Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview](https://mlsanity.com/paper/2202.13564/) (2022)
- [Neural Simulated Annealing](https://mlsanity.com/paper/2203.02201/) (2022)
- [Bayesian Optimization for Macro Placement](https://mlsanity.com/paper/2207.08398/) (2022)
- [Non-Overlapping Placement of Macro Cells based on Reinforcement Learning in Chip Design](https://mlsanity.com/paper/2407.18499/) (2024)
- [RollPlace: Improving Macro Placement via Monte Carlo Rollout Search](https://mlsanity.com/paper/2610.06316/) (2026)

## Abstract (arXiv)

Physical design and production of Integrated Circuits (IC) is becoming increasingly more challenging as the sophistication in IC technology is steadily increasing. Placement has been one of the most critical steps in IC physical design. Through decades of research, partition-based, analytical-based and annealing-based placers have been enriching the placement solution toolbox. However, open challenges including long run time and lack of ability to generalize continue to restrict wider applications of existing placement tools. We devise a learning-based placement tool based on cyclic application of Reinforcement Learning (RL) and Simulated Annealing (SA) by leveraging the advancement of RL. Results show that the RL module is able to provide a better initialization for SA and thus leads to a better final placement design. Compared to other recent learning-based placers, our method is majorly different with its combination of RL and SA. It leverages the RL model's ability to quickly get a good rough solution after training and the heuristic's ability to realize greedy improvements in the solution.