# Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor

arXiv: https://arxiv.org/abs/2411.14296 · HTML: https://mlsanity.com/paper/2411.14296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://mlsanity.com/paper/1904.00420/) (2019)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [Neural Predictor for Neural Architecture Search](https://mlsanity.com/paper/1912.00848/) (2019)
- [LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction](https://mlsanity.com/paper/2203.12831/) (2022)
- [Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1904.07077/) (2019)
- [Qplacer: Frequency-Aware Component Placement for Superconducting Quantum Computers](https://mlsanity.com/paper/2401.17450/) (2024)
- [Automatic Routability Predictor Development Using Neural Architecture Search](https://mlsanity.com/paper/2012.01737/) (2020)
- [Lithography Hotspot Detection and Mitigation in Nanometer VLSI](https://mlsanity.com/paper/1402.3150/) (2014)
- [LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search](https://mlsanity.com/paper/2307.03110/) (2023)

## Abstract (arXiv)

Routability optimization in modern EDA tools has benefited greatly from using machine learning (ML) models. Constructing and optimizing the performance of ML models continues to be a challenge. Neural Architecture Search (NAS) serves as a tool to aid in the construction and improvement of these models. Traditional NAS techniques struggle to perform well on routability prediction as a result of two primary factors. First, the separation between the training objective and the search objective adds noise to the NAS process. Secondly, the increased variance of the search objective further complicates performing NAS. We craft a novel NAS technique, coined SOAP-NAS, to address these challenges through novel data augmentation techniques and a novel combination of one-shot and predictor-based NAS. Results show that our technique outperforms existing solutions by 40% closer to the ideal performance measured by ROC-AUC (area under the receiver operating characteristic curve) in DRC hotspot detection. SOAPNet is able to achieve an ROC-AUC of 0.9802 and a query time of only 0.461 ms.