A New Benchmark for the Appropriate Evaluation of RTL Code Optimization

2026-01-05

arXiv.org

AI Summary

This work proposes RTL-OPT, a new benchmark addressing the limitations of existing evaluations that primarily focus on syntactic correctness of RTL code while inadequately assessing power, performance, and area (PPA) optimization quality. RTL-OPT comprises 36 handcrafted digital circuit tasks, each paired with unoptimized and expert-optimized RTL implementations. The benchmark introduces an end-to-end automated evaluation pipeline that integrates formal functional equivalence checking with quantitative PPA analysis. It enables the first systematic assessment of large language models’ capabilities in RTL optimization, incorporating optimization patterns reflective of industrial practice and capturing dimensions often overlooked by conventional synthesis tools. RTL-OPT thus provides a standardized, quantifiable platform for evaluating LLM-driven hardware design optimization.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Machine Learning: Optimization
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The rapid progress of artificial intelligence increasingly relies on efficient integrated circuit (IC) design. Recent studies have explored the use of large language models (LLMs) for generating Register Transfer Level (RTL) code, but existing benchmarks mainly evaluate syntactic correctness rather than optimization quality in terms of power, performance, and area (PPA). This work introduces RTL-OPT, a benchmark for assessing the capability of LLMs in RTL optimization. RTL-OPT contains 36 handcrafted digital designs that cover diverse implementation categories including combinational logic, pipelined datapaths, finite state machines, and memory interfaces. Each task provides a pair of RTL codes, a suboptimal version and a human-optimized reference that reflects industry-proven optimization patterns not captured by conventional synthesis tools. Furthermore, RTL-OPT integrates an automated evaluation framework to verify functional correctness and quantify PPA improvements, enabling standardized and meaningful assessment of generative models for hardware design optimization.

Problem

• RTL optimization
• large language models
• PPA evaluation
• hardware design
• benchmark

Innovation

• RTL optimization
• large language models
• hardware design benchmark
• PPA evaluation
• automated verification