# BOiLS: Bayesian Optimisation for Logic Synthesis

arXiv: https://arxiv.org/abs/2111.06178 · HTML: https://mlsanity.com/paper/2111.06178/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Based Reinforcement Learning for Atari](https://mlsanity.com/paper/1903.00374/) (2019)
- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [BOSS: Bayesian Optimization over String Spaces](https://mlsanity.com/paper/2010.00979/) (2020)
- [Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces](https://mlsanity.com/paper/2102.07188/) (2021)
- [DRiLLS: Deep Reinforcement Learning for Logic Synthesis](https://mlsanity.com/paper/1911.04021/) (2019)
- [Developing Synthesis Flows Without Human Knowledge](https://mlsanity.com/paper/1804.05714/) (2018)

## Built on by (most cited first, 18 of 18)

- [LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2401.12224/) (2023)
- [Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?](https://mlsanity.com/paper/2301.12844/) (2023)
- [Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization](https://mlsanity.com/paper/2306.09803/) (2023)
- [Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal](https://mlsanity.com/paper/2410.10384/) (2024)
- [A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design](https://mlsanity.com/paper/2309.03208/) (2023)
- [Circuit Transformer: A Transformer That Preserves Logical Equivalence](https://mlsanity.com/paper/2403.13838/) (2024)
- [OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis](https://mlsanity.com/paper/2411.09422/) (2024)
- [Rethinking Reinforcement Learning based Logic Synthesis](https://mlsanity.com/paper/2205.07614/) (2022)
- [Structured Q-learning For Antibody Design](https://mlsanity.com/paper/2209.04698/) (2022)
- [Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning](https://mlsanity.com/paper/2312.14536/) (2023)
- [ShortCircuit: AlphaZero-Driven Circuit Design](https://mlsanity.com/paper/2408.09858/) (2024)
- [Fine-tuning LLMs with variational Bayesian last layer for high-dimensional Bayesian optimization](https://mlsanity.com/paper/2510.01471/) (2025)
- [An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning](https://mlsanity.com/paper/2403.17395/) (2024)
- [GENIAL: Generative Design Space Exploration via Network Inversion for Low Power Algorithmic Logic Units](https://mlsanity.com/paper/2507.18989/) (2025)
- [ELF: Efficient Logic Synthesis by Pruning Redundancy in Refactoring](https://mlsanity.com/paper/2508.08073/) (2025)
- [A reproducible comparative study of categorical kernels for Gaussian process regression, with new clustering-based nested kernels](https://mlsanity.com/paper/2510.01840/) (2025)
- [SynAct: A Reasoning-Acting Large Language Model Agent for Adaptive Synthesis Optimization](https://mlsanity.com/paper/2608.12751/) (2026)
- [SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization](https://mlsanity.com/paper/2608.14579/) (2026)

## Abstract (arXiv)

Optimising the quality-of-results (QoR) of circuits during logic synthesis is a formidable challenge necessitating the exploration of exponentially sized search spaces. While expert-designed operations aid in uncovering effective sequences, the increase in complexity of logic circuits favours automated procedures. Inspired by the successes of machine learning, researchers adapted deep learning and reinforcement learning to logic synthesis applications. However successful, those techniques suffer from high sample complexities preventing widespread adoption. To enable efficient and scalable solutions, we propose BOiLS, the first algorithm adapting modern Bayesian optimisation to navigate the space of synthesis operations. BOiLS requires no human intervention and effectively trades-off exploration versus exploitation through novel Gaussian process kernels and trust-region constrained acquisitions. In a set of experiments on EPFL benchmarks, we demonstrate BOiLS's superior performance compared to state-of-the-art in terms of both sample efficiency and QoR values.