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

Abstract--Chip placement is a vital stage in modern chip design as it has a substantial impact on the subsequent processes and the overall quality of the final chip. The use of black-box optimization (BBO) for chip placement has a history of several decades. However, early efforts were limited by immature problem formulations and inefficient algorithm designs, leading to suboptimal efficiency, quality, and scalability, compared to the more prevalent analytical methods. Recent progress in problem formulation and algorithm design has shown the effectiveness and efficiency of BBO for chip placement, proving its potential to achieve state-of-the-art results. Despite these advancements, the field lacks a unified, BBO-specific benchmark for thoroughly assessing various problem formulations and BBO algorithms. T o fill this gap, we propose BBOPlace-Bench, the first benchmark designed specifically for evaluating and developing BBO algorithms for chip placement tasks. It integrates three problem formulations (with permutation, continuous, and mixed search spaces, respectively) of BBO for chip placement, and offers a modular, decoupled, and flexible framework that enables users to seamlessly implement, test, and compare their own algorithms. BBOPlace-Bench aggregates modern chip cases from representative chip cases (ISPD 2005, ICCAD 2015) and standardizes their formats, providing uniform and comprehensive information to support BBO optimization. Moreover, it integrates a wide variety of existing BBO algorithms, including simulated annealing (SA), evolutionary algorithms (EAs), and Bayesian optimization (BO), and systematically evaluates their performance across different problem formulations using key metrics (e.g., macro placement wirelength and global placement wirelength) of chip. Experimental results show that the problem formulations of mask-guided optimization and hyperparameter optimization exhibit superior performance than the sequence pair problem formulation, while EAs demonstrate better overall performance than SA and BO, especially in high-dimensional search spaces, and also achieve state-of-the-art performance compared to the mainstream chip placement methods, i.e., analytical methods and reinforcement learning methods. BBOPlace-Bench not only facilitates the development of efficient BBO-driven solutions for chip placement but also broadens the practical application scenarios (which are urgently needed) for the BBO community. The code of BBOPlace-Bench is available at https://github.com/ The first three authors contributed equally.