# BBOPlace-Bench: Benchmarking Black-Box Optimization for Chip Placement

arXiv: https://arxiv.org/abs/2510.23472 · HTML: https://mlsanity.com/paper/2510.23472/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 95 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 30 of 34)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model](https://mlsanity.com/paper/2401.02051/) (2024)
- [High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces](https://mlsanity.com/paper/2103.00349/) (2021)
- [Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization](https://mlsanity.com/paper/2202.08450/) (2022)
- [LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics](https://mlsanity.com/paper/2405.20132/) (2024)
- [Vanilla Bayesian Optimization Performs Great in High Dimensions](https://mlsanity.com/paper/2402.02229/) (2024)
- [Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search](https://mlsanity.com/paper/2007.00708/) (2020)
- [A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization](https://mlsanity.com/paper/2111.05040/) (2021)
- [HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML](https://mlsanity.com/paper/2106.06257/) (2021)
- [A Systematic Survey on Large Language Models for Algorithm Design](https://mlsanity.com/paper/2410.14716/) (2024)
- [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)
- [Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization](https://mlsanity.com/paper/2210.01628/) (2022)
- [Multi-agent Dynamic Algorithm Configuration](https://mlsanity.com/paper/2210.06835/) (2022)
- [MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning](https://mlsanity.com/paper/2310.08252/) (2023)
- [Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization](https://mlsanity.com/paper/2411.00625/) (2024)
- [Macro Placement by Wire-Mask-Guided Black-Box Optimization](https://mlsanity.com/paper/2306.16844/) (2023)
- [ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning](https://mlsanity.com/paper/2412.07507/) (2024)
- [Offline Multi-Objective Optimization](https://mlsanity.com/paper/2406.03722/) (2024)
- [Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithms](https://mlsanity.com/paper/2407.15026/) (2024)
- [Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing](https://mlsanity.com/paper/2011.07577/) (2020)
- [DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale](https://mlsanity.com/paper/2502.01681/) (2025)
- [Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization](https://mlsanity.com/paper/2408.10672/) (2024)
- [Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer](https://mlsanity.com/paper/2412.07167/) (2024)
- [PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization](https://mlsanity.com/paper/2212.05652/) (2022)
- [That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design](https://mlsanity.com/paper/2411.10053/) (2024)

## Built on by (most cited first, 5 of 5)

- [Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution](https://mlsanity.com/paper/2606.08904/) (2026)
- [PPAPlace: Differentiable Cross-Stage Objectives for Chip Placement Optimization](https://mlsanity.com/paper/2608.13790/) (2026)
- [Rethinking Learnability in Offline Data-driven Optimization](https://mlsanity.com/paper/2609.01493/) (2026)
- [Agentic High-Dimensional Bayesian Optimization with Hypothesis- and Evidence-Guided Search](https://mlsanity.com/paper/2609.34281/) (2026)
- [Trinity: One Differentiable Physics for Training, Refining and Scoring Generative Floorplanners](https://mlsanity.com/paper/2610.04957/) (2026)

## Abstract (arXiv)

Chip placement is a vital stage in modern chip design, and black-box optimization (BBO) has been applied to it for decades. Early BBO efforts, however, were limited by immature problem formulations and inefficient algorithm designs, leading to worse efficiency, quality, and scalability than mainstream analytical methods. Recent advances in BBO have shown strong potential, but a unified, BBO-specific benchmark for thoroughly assessing various problem formulations and BBO algorithms is lacking. To fill this gap, we propose BBOPlace-Bench, the first benchmark tailored for evaluating and developing BBO algorithms for chip placement. It integrates three BBO problem formulations and offers a modular, flexible framework that enables users to seamlessly implement, test, and compare their own algorithms. It aggregates representative modern chip cases and standardizes their formats, providing uniform and comprehensive information to support BBO optimization. Moreover, it integrates representative BBO algorithm families, including simulated annealing, population-based search (including GA, CMA-ES, and PSO), and Bayesian optimization, and systematically evaluates their performance across different problem formulations using key chip-placement metrics. We position these experiments primarily as illustrative case studies under a shared evaluation protocol, including common benchmark instances, metric definitions, evaluation pipeline, and search budgets. Under this protocol, some BBO configurations (e.g., GA under the mask-guided optimization formulation) are competitive with representative analytical and reinforcement learning baselines. BBOPlace-Bench not only facilitates the development of efficient BBO-driven solutions for chip placement but also broadens the practical application scenarios urgently needed by the BBO community.