# Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview

arXiv: https://arxiv.org/abs/2202.13564 · HTML: https://mlsanity.com/paper/2202.13564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 97 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [On Joint Learning for Solving Placement and Routing in Chip Design](https://mlsanity.com/paper/2111.00234/) (2021)
- [A Deep Reinforcement Learning Approach for Global Routing](https://mlsanity.com/paper/1906.08809/) (2019)
- [Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing](https://mlsanity.com/paper/2011.07577/) (2020)
- [Attention Routing: track-assignment detailed routing using attention-based reinforcement learning](https://mlsanity.com/paper/2004.09473/) (2020)
- [Circuit Routing Using Monte Carlo Tree Search and Deep Neural Networks](https://mlsanity.com/paper/2006.13607/) (2020)
- [Training a Fully Convolutional Neural Network to Route Integrated Circuits](https://mlsanity.com/paper/1706.08948/) (2017)

## Built on by (most cited first, 4 of 4)

- [MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning](https://mlsanity.com/paper/2211.13382/) (2022)
- [Macro Placement by Wire-Mask-Guided Black-Box Optimization](https://mlsanity.com/paper/2306.16844/) (2023)
- [GOALPlace: Begin with the End in Mind](https://mlsanity.com/paper/2407.04579/) (2024)
- [Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints](https://mlsanity.com/paper/2504.17142/) (2025)

## Abstract (arXiv)

Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows. Compared with traditional solvers using heuristics or expert-well-designed algorithms, machine learning has shown promising prospects by its data-driven nature, which can be of less reliance on knowledge and priors, and potentially more scalable by its advanced computational paradigms (e.g. deep networks with GPU acceleration). This survey starts with the introduction of basics of placement and routing, with a brief description on classic learning-free solvers. Then we present detailed review on recent advance in machine learning for placement and routing. Finally we discuss the challenges and opportunities for future research.