# Rethinking LLM-Based RTL Code Optimization Via Timing Logic Metamorphosis

arXiv: https://arxiv.org/abs/2507.16808 · HTML: https://mlsanity.com/paper/2507.16808/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 68 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## Builds on (most cited first, 22 of 22)

- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [VerilogEval: Evaluating Large Language Models for Verilog Code Generation](https://mlsanity.com/paper/2309.07544/) (2023)
- [VeriGen: A Large Language Model for Verilog Code Generation](https://mlsanity.com/paper/2308.00708/) (2023)
- [RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model](https://mlsanity.com/paper/2308.05345/) (2023)
- [Learning Performance-Improving Code Edits](https://mlsanity.com/paper/2302.07867/) (2023)
- [RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models](https://mlsanity.com/paper/2311.16543/) (2023)
- [SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation](https://mlsanity.com/paper/2308.10873/) (2023)
- [Semantic Robustness of Models of Source Code](https://mlsanity.com/paper/2002.03043/) (2020)
- [Meta Large Language Model Compiler: Foundation Models of Compiler Optimization](https://mlsanity.com/paper/2407.02524/) (2024)
- [RTLRewriter: Methodologies for Large Models aided RTL Code Optimization](https://mlsanity.com/paper/2409.11414/) (2024)
- [ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events](https://mlsanity.com/paper/2501.03040/) (2025)
- [SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning](https://mlsanity.com/paper/2504.10369/) (2025)
- [Language Models for Code Optimization: Survey, Challenges and Future Directions](https://mlsanity.com/paper/2501.01277/) (2025)
- [VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation](https://mlsanity.com/paper/2504.15659/) (2025)
- [Learning Compiler Pass Orders using Coreset and Normalized Value Prediction](https://mlsanity.com/paper/2301.05104/) (2023)
- [Exploiting Code Symmetries for Learning Program Semantics](https://mlsanity.com/paper/2308.03312/) (2023)
- [Priority Sampling of Large Language Models for Compilers](https://mlsanity.com/paper/2402.18734/) (2024)
- [A Novel HDL Code Generator for Effectively Testing FPGA Logic Synthesis Compilers](https://mlsanity.com/paper/2407.12037/) (2024)
- [Optimizing and Evaluating Transient Gradual Typing](https://mlsanity.com/paper/1902.07808/) (2019)
- [Efficient Binary-Level Coverage Analysis](https://mlsanity.com/paper/2004.14191/) (2020)
- [Iterating Pointers: Enabling Static Analysis for Loop-based Pointers](https://mlsanity.com/paper/2503.03359/) (2025)

## Built on by (most cited first, 1 of 1)

- [Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement](https://mlsanity.com/paper/2604.14989/) (2026)

## Abstract (arXiv)

Register Transfer Level(RTL) code optimization is crucial for achieving high performance and low power consumption in digital circuit design. However, traditional optimization methods often rely on manual tuning and heuristics, which can be time-consuming and error-prone. Recent studies proposed to leverage Large Language Models(LLMs) to assist in RTL code optimization. LLMs can generate optimized code snippets based on natural language descriptions, potentially speeding up the optimization process. However, existing approaches have not thoroughly evaluated the effectiveness of LLM-Based code optimization methods for RTL code with complex timing logic. To address this gap, we conducted a comprehensive empirical investigation to assess the capability of LLM-Based RTL code optimization methods in handling RTL code with complex timing logic. In this study, we first propose a new benchmark for RTL optimization evaluation. It comprises four subsets, each corresponding to a specific area of RTL code optimization. Then we introduce a method based on metamorphosis to systematically evaluate the effectiveness of LLM-Based RTL code optimization methods.Our key insight is that the optimization effectiveness should remain consistent for semantically equivalent but more complex code. After intensive experiments, we revealed several key findings. (1) LLM-Based RTL optimization methods can effectively optimize logic operations and outperform existing compiler-based methods. (2) LLM-Based RTL optimization methods do not perform better than existing compiler-based methods on RTL code with complex timing logic, particularly in timing control flow optimization and clock domain optimization. This is primarily attributed to the challenges LLMs face in understanding timing logic in RTL code. Based on these findings, we provide insights for further research in leveraging LLMs for RTL code optimization.