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LLMs evolve chip placement objectives, improving routing and timing

By PulseAugur Editorial · [3 sources] ·

Researchers have developed CoEvoP&R, a novel framework that leverages large language models (LLMs) to automatically evolve placement objectives for analytical placers. This approach addresses the misalignment between traditional placement-stage surrogate metrics and downstream routing and timing quality. By generating readable, differentiable objectives and validating them with routing feedback, CoEvoP&R significantly improves post-route wirelength, congestion, and timing metrics compared to existing methods. AI

IMPACT This research could lead to more efficient chip design by improving the accuracy of placement algorithms through LLM-driven objective evolution.

RANK_REASON The cluster contains a research paper detailing a new methodology for evolving placement objectives using LLMs.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

LLMs evolve chip placement objectives, improving routing and timing

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The cluster contains a research paper detailing a new methodology for evolving placement objectives using LLMs.
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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Kabir Murjani, Mishri Bhavsar, Manish I. Patel, Jonti Talukdar ·

    AlphaRoute: Large Language Models as Semantic Optimizers for Multi-Objective Routing

    arXiv:2607.19768v1 Announce Type: new Abstract: Very Large Scale Integration (VLSI) global routing is an NP-hard combinatorial optimization problem requiring signal net assignment across capacity-constrained 3D grids while minimizing congestion, wirelength, and via transitions. B…

  2. arXiv cs.AI TIER_1 English(EN) · Ruogu Chen, Weihua Xiao, Ramesh Karri, Jie Han ·

    CoEvoP&R: Co-Evolving Placement Objectives with Routing Feedback via Large Language Models

    arXiv:2607.17398v1 Announce Type: cross Abstract: Analytical placers rely on differentiable objective functions to guide placement, typically combining intermediate surrogate metrics such as half-perimeter wirelength (HPWL) and cell-density penalties. However, these placement-sta…

  3. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Jie Han ·

    CoEvoP&R: Co-Evolving Placement Objectives with Routing Feedback via Large Language Models

    Analytical placers rely on differentiable objective functions to guide placement, typically combining intermediate surrogate metrics such as half-perimeter wirelength (HPWL) and cell-density penalties. However, these placement-stage surrogates remain misaligned with downstream ro…

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