FIXME: Towards End-to-End Benchmarking of LLM-Aided Design Verification

TL;DR

This paper introduces FIXME, an open-source, end-to-end benchmarking framework for evaluating large language models in hardware design verification, addressing a critical gap in functional verification assessment.

Contribution

It presents the first comprehensive, multi-level, and real-world dataset-based framework for assessing LLMs in hardware verification, including a structured difficulty hierarchy and expert-guided coverage enhancement.

Findings

LLMs like GPT-4 and Claude3 show promising capabilities but need improvement.

The framework covers 180 diverse tasks across six verification sub-domains.

Functional coverage improved by 45.57% through expert-guided optimization.

Abstract

Despite the transformative potential of Large Language Models (LLMs) in hardware design, a comprehensive evaluation of their capabilities in design verification remains underexplored. Current efforts predominantly focus on RTL generation and basic debugging, overlooking the critical domain of functional verification, which is the primary bottleneck in modern design methodologies due to the rapid escalation of hardware complexity. We present FIXME, the first end-to-end, multi-model, and open-source evaluation framework for assessing LLM performance in hardware functional verification (FV) to address this crucial gap. FIXME introduces a structured three-level difficulty hierarchy spanning six verification sub-domains and 180 diverse tasks, enabling in-depth analysis across the design lifecycle. Leveraging a collaborative AI-human approach, we construct a high-quality dataset using 100% silicon-proven designs, ensuring comprehensive coverage of real-world challenges. Furthermore, we enhance the functional coverage by 45.57% through expert-guided optimization. By rigorously evaluating state-of-the-art LLMs such as GPT-4, Claude3, and LlaMA3, we identify key areas for improvement and outline promising research directions to unlock the full potential of LLM-driven automation in hardware design verification. The benchmark is available at https://github.com/ChatDesignVerification/FIXME.