# Customized Routing Optimization Based on Gradient Boost Regressor Model

arXiv: https://arxiv.org/abs/1710.11118 · HTML: https://mlsanity.com/paper/1710.11118/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Minimum Bisection Problem: Machine Learning-Based Penalty Parameter Tuning for Optimization on Quantum Annealers](https://mlsanity.com/paper/2509.19005/) (2025)

## Abstract (arXiv)

In this paper, we discussed limitation of current electronic-design-automoation (EDA) tool and proposed a machine learning framework to overcome the limitations and achieve better design quality. We explored how to efficiently extract relevant features and leverage gradient boost regressor (GBR) model to predict underestimated risky net (URN). Customized routing optimizations are applied to the URNs and results show clear timing improvement and trend to converge toward timing closure.