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.
Llm-aided efficient hardware design automation
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Proposes five foundational pillars and architectural patterns for building robust GenAI-native systems by combining AI with software engineering principles.
citing papers explorer
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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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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.