RTL-BenchMT: Dynamic Maintenance of RTL Generation Benchmark Through Agent-Assisted Analysis and Revision

TL;DR

RTL-BenchMT is an automated, agentic framework designed to dynamically maintain RTL generation benchmarks by identifying and revising flawed and overfitting cases, reducing manual effort and improving benchmark quality.

Contribution

It introduces an automated framework for systematic detection and revision of flawed and overfitting benchmark cases in RTL generation, enhancing benchmark reliability.

Findings

Refined benchmark suite produced and open-sourced.

Automated identification of flawed and overfitting cases.

Reduced human maintenance effort.

Abstract

This paper introduces RTL-BenchMT, an agentic framework for dynamically maintaining RTL generation benchmarks. Large Language Models (LLMs) assisted automated RTL generation is one of the most important directions in EDA research. However, current RTL benchmarks face two critical challenges: (1) flawed cases in the benchmarks and (2) overfitting to the benchmarks. Both challenges are difficult to resolve purely by manual engineering effort. To address these issues and systematically reduce human maintenance costs, we propose an automated agentic framework, RTL-BenchMT. RTL-BenchMT focuses on two key applications: (1) automatically identifying and revising flawed benchmark cases and (2) automatically detecting and updating overfitting cases. With the assistance of RTL-BenchMT, we conduct a thorough, in-depth analysis of flawed and overfitting cases and produce a refined benchmark suite that will be open-sourced to the community.