LLM-Augmented FPGA Timing Closure: Toward Intelligent Static Timing Analysis Agents

Saher Elsayed

Keywords: FPGA, Static Timing Analysis, Large Language Models, EDA, Timing Closure, Machine Learning

TL;DR: No more STA violations, maybe?

Abstract: Achieving timing closure in modern FPGA designs remains a significant bottleneck in the electronic design automation (EDA) workflow, often requiring extensive manual intervention and iterative refinement by experienced engineers. We propose TimingLLM, a novel framework that leverages Large Language Models (LLMs) augmented with Retrieval-Augmented Generation (RAG) to automate and accelerate FPGA static timing analysis (STA). Our approach integrates domain-specific knowledge bases containing FPGA architectural specifications, timing constraints, and historical optimization strategies to enable LLM-driven timing violation diagnosis and automated fix recommendation. We evaluate TimingLLM on 12 production-representative FPGA designs across networking, signal processing, and AI accelerator applications, comprising 658 timing violations. Experimental results demonstrate that TimingLLM achieves 82% F1 score in violation root cause classification, with particularly strong performance on clock domain crossing (100% F1) and architectural bottleneck detection (97% F1). LLM-generated constraint fixes reduce worst negative slack by 56% on average. Extended experiments show stable performance up to 1,200 violations with sub-3ms inference time, and cross-domain transfer analysis reveals ~20% degradation when generalizing across application domains. This work establishes the foundation for autonomous timing closure agents that can significantly reduce design iteration cycles.

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Submission Number: 2