OpenRTLSet supplies 131k+ Verilog samples with AI-generated descriptions to enable fine-tuning of LLMs for hardware module design.
Llm-aided efficient hardware design automation
5 Pith papers cite this work. Polarity classification is still indexing.
representative citing papers
TimingLLM uses a fine-tuned LLM to generate structural timing cues from Verilog followed by a retrieval-augmented regressor with a learned steering vector to predict WNS and TNS with R values of 0.91 and 0.97.
VeriPilot raises GPT-4o Verilog repair success from 54.3% to 85.71% on the CVDP benchmark by using golden-model semantic alignment and CDFG-based signal tracing.
Proposes five foundational pillars and architectural patterns for building robust GenAI-native systems by combining AI with software engineering principles.
Survey of GenAI in PCB design lifecycle presenting taxonomy, technical challenges, and research directions.
citing papers explorer
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OpenRTLSet: A Fully Open-Source Dataset for Large Language Model-based Verilog Module Design
OpenRTLSet supplies 131k+ Verilog samples with AI-generated descriptions to enable fine-tuning of LLMs for hardware module design.
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TimingLLM: A Two-Stage Retrieval-Augmented Framework for Pre-Synthesis Timing Prediction from Verilog
TimingLLM uses a fine-tuned LLM to generate structural timing cues from Verilog followed by a retrieval-augmented regressor with a learned steering vector to predict WNS and TNS with R values of 0.91 and 0.97.
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VeriPilot: An LLM-Powered Verilog Debugging Framework
VeriPilot raises GPT-4o Verilog repair success from 54.3% to 85.71% on the CVDP benchmark by using golden-model semantic alignment and CDFG-based signal tracing.
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Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems
Proposes five foundational pillars and architectural patterns for building robust GenAI-native systems by combining AI with software engineering principles.
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Surveying GenAI-based Automation in Printed Circuit Board Design and Test
Survey of GenAI in PCB design lifecycle presenting taxonomy, technical challenges, and research directions.