Bridging the Last Mile of Circuit Design: PostEDA-Bench, a Hierarchical Benchmark for PPA Convergence and DRC Fixing
arXiv:2605.06936v2 Announce Type: replace-cross Abstract: LLM-based agents are increasingly applied to the "last mile" of Electronic Design Automation (EDA): repairing residual sign-off Design Rule Check (DRC) violations and converging Power-Performance-Area (PPA) targets after tool runs. Existing EDA-LLM benchmarks, however, omit DRC fixing entirely and rely on flat hierarchies tied to a single toolchain. We introduce PostEDA-Bench, a hierarchical benchmark with 145 tasks across DRC-Essential, DRC-Reasoning, PPA-Mono, and PPA-Multi, supported by EDA toolchains with machine-checkable evaluatio
The increasing sophistication of LLMs is pushing their application into highly specialized engineering fields like Electronic Design Automation, where traditional methods struggle with optimization at the 'last mile'.
This development indicates a significant advancement in AI's ability to automate complex design tasks, accelerating chip development cycles and improving efficiency in a critical industry.
The explicit introduction of hierarchical benchmarks for PPA convergence and DRC fixing signifies a more robust and testable framework for evaluating AI agents in EDA, moving beyond previous flat and incomplete benchmarks.
- · Chip design companies
- · EDA software providers leveraging AI
- · AI agent developers
- · Manual chip verification engineers (long-term)
- · Traditional EDA tool developers resistant to AI
AI agents will become increasingly adept at identifying and resolving design flaws in semiconductors, reducing time-to-market.
The improved efficiency and reduced errors in chip design could lead to more complex and powerful chips being developed faster, impacting other tech sectors.
As AI optimizes the design and manufacturing of semiconductors, it could indirectly reduce the energy footprint and cost of advanced compute, influencing the energy bottleneck narrative.
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Read at arXiv cs.AI