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New benchmark BBOPlace-Bench advances AI for chip placement

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced BBOPlace-Bench, a novel benchmark designed to evaluate and advance black-box optimization (BBO) algorithms specifically for chip placement tasks. This benchmark addresses a gap in existing tools by providing a unified framework that integrates various BBO problem formulations and standardizes chip cases for comprehensive algorithm assessment. It includes representative BBO algorithm families such as simulated annealing, population-based search, and Bayesian optimization, enabling systematic performance comparisons against analytical and reinforcement learning baselines. AI

IMPACT This benchmark aims to accelerate the development of efficient AI-driven solutions for chip placement, potentially improving the performance and scalability of chip design processes.

RANK_REASON The cluster contains a research paper introducing a new benchmark for AI algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark BBOPlace-Bench advances AI for chip placement

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The cluster contains a research paper introducing a new benchmark for AI algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ke Xue, Ruo-Tong Chen, Rong-Xi Tan, Xi Lin, Yunqi Shi, Siyuan Xu, Mingxuan Yuan, Chao Qian ·

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

    arXiv:2510.23472v2 Announce Type: replace-cross Abstract: 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 algorith…

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