Dr. RTL's multi-agent framework with group-relative skill learning achieves 21% WNS and 17% TNS timing improvements plus 6% area reduction on 20 real-world RTL designs over commercial synthesis tools.
VerilogEval: Evaluating Large Language Models for Verilog Code Generation
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ChipLingo trains LLMs on EDA data via corpus construction, domain-adaptive pretraining, and RAG scenario alignment, reaching 59.7% accuracy with an 8B model and 70.02% with a 32B model on a new internal EDA benchmark.
Workshop report recommends NSF investments in AI-EDA collaboration, data infrastructure, compute resources, and workforce development to accelerate hardware design.
citing papers explorer
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Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement
Dr. RTL's multi-agent framework with group-relative skill learning achieves 21% WNS and 17% TNS timing improvements plus 6% area reduction on 20 real-world RTL designs over commercial synthesis tools.
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ChipLingo: A Systematic Training Framework for Large Language Models in EDA
ChipLingo trains LLMs on EDA data via corpus construction, domain-adaptive pretraining, and RAG scenario alignment, reaching 59.7% accuracy with an 8B model and 70.02% with a 32B model on a new internal EDA benchmark.
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Report for NSF Workshop on AI for Electronic Design Automation
Workshop report recommends NSF investments in AI-EDA collaboration, data infrastructure, compute resources, and workforce development to accelerate hardware design.