RTL++: Graph-enhanced LLM for RTL Code Generation

• URL: http://arxiv.org/abs/2505.13479v1
• Date: Sun, 11 May 2025 00:17:26 GMT
• Title: RTL++: Graph-enhanced LLM for RTL Code Generation
• Abstract summary: Traditional register transfer level (RTL) design methods are manual, time-consuming, and prone to errors.<n>Open-source models offer alternatives; however, they frequently fall short in quality/correctness.<n>This paper proposes RTL++, a first-of-its-kind LLM-assisted method for RTL code generation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As hardware design complexity escalates, there is an urgent need for advanced automation in electronic design automation (EDA). Traditional register transfer level (RTL) design methods are manual, time-consuming, and prone to errors. While commercial (instruction-tuned) large language models (LLMs) shows promising performance for automation, they pose security and privacy concerns. Open-source models offer alternatives; however, they frequently fall short in quality/correctness, largely due to limited, high-quality RTL code data essential for effective training and generalization. This paper proposes RTL++, a first-of-its-kind LLM-assisted method for RTL code generation that utilizes graph representations of code structures to enhance the quality of generated code. By encoding RTL code into a textualized control flowgraphs (CFG) and data flow graphs (DFG), RTL++ captures the inherent hierarchy, dependencies, and relationships within the code. This structured graph-based approach enhances the context available to LLMs, enabling them to better understand and generate instructions. By focusing on data generation through graph representations, RTL++ addresses the limitations of previous approaches that rely solely on code and suffer from lack of diversity. Experimental results demonstrate that RTL++ outperforms state-of-the-art models fine-tuned for RTL generation, as evaluated using the VerilogEval benchmark's Pass@1/5/10 metric, as well as the RTLLM1.1 model, which highlight the effectiveness of graph-enhanced context in advancing the capabilities of LLM-assisted RTL code generation.

Related papers

• LLM4RTL: Tool-Assisted LLM for RTL Generation [5.338291061810534]
  Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems.<n>We propose a judge-renew-check-renew-check'' pipeline which updates a current public dataset using a hierarchy of state-of-the-art commercial LLM models.<n>Our experiments identify some common weaknesses of LLMs in rule-based reasoning and logic, and consequently, in RTL code-generation.
  arXiv  Detail & Related papers  (2026-06-13T23:10:40Z)
• CASS-RTL: Correctness-Aware Subspace Steering for RTL Generation with LLMs [0.0]
  This work proposes CASS-RTL, a first-of-its-kind framework for discovering and leveraging LLMs' correctness-aware components.<n>We observe 10%-20% improvement in pass@1/5/10 accuracy on VerilogEval and 5% improvement on CVDP.
  arXiv  Detail & Related papers  (2026-06-04T04:02:51Z)
• EstRTL: Functional Estimation Guided RTL Code Generation [11.81361680232628]
  EstRTL is a collaborative agent framework for RTL code generation based on static functional score estimation.<n> EstRTL significantly improves the correctness of RTL code generation by generic LLM by 3.2%-9.0%.
  arXiv  Detail & Related papers  (2026-06-01T03:07:31Z)
• UniRTL: Unifying Code and Graph for Robust RTL Representation Learning [25.37890373797555]
  We propose UniRTL, a multimodal pretraining framework that learns unified RTL representations by jointly leveraging code and CDFG.<n>UniRTL consistently outperforms prior methods, establishing it as a more robust and powerful foundation for advancing hardware design automation.
  arXiv  Detail & Related papers  (2026-05-29T09:18:23Z)
• RTLSeek: Boosting the LLM-Based RTL Generation with Multi-Stage Diversity-Oriented Reinforcement Learning [10.116061321343404]
  LLM-based RTL generation is promising, but the scarcity of functionally verifiable high-quality data limits both accuracy and diversity.<n>We propose RTLSeek, a post-training paradigm that applies rule-based Diversity-Oriented Reinforcement Learning to improve RTL correctness and diversity.<n>Our Diversity-Centric Multi-Objective Reward Scheduling integrates expert knowledge with EDA feedback, and a three-stage framework maximizes the utility of limited data.
  arXiv  Detail & Related papers  (2026-03-29T11:01:02Z)
• Assessing Large Language Models in Generating RTL Design Specifications [2.4125580419022477]
  Engineers currently should manually interpret existing RTL code and write specifications, a slow and error-prone process.<n>We investigate how prompting strategies affect RTL-to-specification quality and introduce metrics for faithfully evaluating generated specs.
  arXiv  Detail & Related papers  (2025-11-17T10:06:24Z)
• DeepV: A Model-Agnostic Retrieval-Augmented Framework for Verilog Code Generation with a High-Quality Knowledge Base [13.906575706979375]
  We introduce DeepV: a model-agnostic RAG framework to generate RTL designs by enhancing context through a large, high-quality dataset without any RTL-specific training.<n>Our framework benefits the latest commercial LLM, OpenAI's GPT-5, with a near 17% increase in performance on the VerilogEval benchmark.
  arXiv  Detail & Related papers  (2025-10-06T19:47:27Z)
• ChipSeek-R1: Generating Human-Surpassing RTL with LLM via Hierarchical Reward-Driven Reinforcement Learning [32.11086992218369]
  ChipSeek-R1 is a hierarchical reward-driven reinforcement learning framework for large language models.<n>It generates RTL code that is both functional correctness and PPA-optimized.<n>On the RTLLM benchmark, ChipSeek-R1 generated 27 RTL designs surpassing the PPA metrics of the original human-written code.
  arXiv  Detail & Related papers  (2025-07-07T08:08:20Z)
• DecoRTL: A Run-time Decoding Framework for RTL Code Generation with LLMs [0.0]
  We show that large language models (LLMs) exhibit low confidence in regions of structural ambiguity or semantic complexity.<n>We introduce DecoRTL, a novel run-time decoding strategy, that is both syntax-aware and contrastive for RTL code generation.<n>Our approach operates entirely at inference time without requiring any additional model fine-tuning.
  arXiv  Detail & Related papers  (2025-07-03T01:17:44Z)
• ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation [4.965247405975508]
  We introduce ScaleRTL, the first reasoning LLM for RTL coding that scales up both high-quality reasoning data and test-time compute.<n>Specifically, we curate a diverse set of long chain-of-thought reasoning traces averaging 56K tokens each, resulting in a dataset of 3.5B tokens that captures rich RTL knowledge.<n>Fine-tuning a general-purpose reasoning model on this corpus yields ScaleRTL that is capable of deep RTL reasoning.
  arXiv  Detail & Related papers  (2025-06-05T20:24:58Z)
• QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation [51.393569044134445]
  Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification.<n> Extending RLVR to automatically generating hardware description languages (HDLs) like Verilog from natural-language (NL) specifications, however, poses three key challenges.<n>We introduce CodeV-R1, an RLVR framework for training Verilog generation LLMs.
  arXiv  Detail & Related papers  (2025-05-30T03:51:06Z)
• RTLRepoCoder: Repository-Level RTL Code Completion through the Combination of Fine-Tuning and Retrieval Augmentation [6.428086269916113]
  We propose RTLRepoCoder, a groundbreaking solution that incorporates specific fine-tuning and Retrieval-Augmented Generation (RAG) for repository-level Verilog code completion.<n>Our solution achieves state-of-the-art performance on public benchmark, significantly surpassing GPT-4 and advanced domain-specific LLMs on Edit Similarity and Exact Match rate.
  arXiv  Detail & Related papers  (2025-04-11T09:04:50Z)
• MAGE: A Multi-Agent Engine for Automated RTL Code Generation [5.899673582879575]
  MAGE is the first open-source multi-agent AI system designed for robust and accurate Verilog RTL code generation.<n>MAGE achieves a 95.7% rate of syntactic and functional correctness code generation on VerilogEval-Human 2 benchmark.
  arXiv  Detail & Related papers  (2024-12-10T21:53:55Z)
• EDA-Aware RTL Generation with Large Language Models [0.7831852829409273]
  Large Language Models (LLMs) have become increasingly popular for generating RTL code.<n> producing error-free RTL code in a zero-shot setting remains highly challenging for even state-of-the-art LLMs.<n>We introduce AIvril2, a self-verifying, LLM-agnostic agentic framework aimed at enhancing RTL code generation through iterative corrections of both syntax and functional errors.
  arXiv  Detail & Related papers  (2024-11-21T00:37:51Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• AIvril: AI-Driven RTL Generation With Verification In-The-Loop [0.7831852829409273]
  Large Language Models (LLMs) are computational models capable of performing complex natural language processing tasks. This paper introduces AIvril, a framework designed to enhance the accuracy and reliability of RTL-aware LLMs.
  arXiv  Detail & Related papers  (2024-09-03T15:07:11Z)
• OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection [54.775409528658486]
  OriGen is a fully open-source framework that incorporates self-reflection capabilities and a novel dataset augmentation methodology. Our approach employs a code-tocode augmentation technique to enhance the quality of open-source RTL code datasets.
  arXiv  Detail & Related papers  (2024-07-23T07:22:25Z)
• Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models [54.14602121129874]
  We introduce AutoIF, the first scalable and reliable method for automatically generating instruction-following training data. AutoIF transforms the validation of instruction-following data quality into code verification.
  arXiv  Detail & Related papers  (2024-06-19T13:29:53Z)
• CodecLM: Aligning Language Models with Tailored Synthetic Data [51.59223474427153]
  We introduce CodecLM, a framework for adaptively generating high-quality synthetic data for instruction-following abilities. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples.
  arXiv  Detail & Related papers  (2024-04-08T21:15:36Z)
• Exploring Data-Efficient Adaptation of Large Language Models for Code Generation [64.5583894165813]
  We propose a novel adaptation approach named DEED, which stands for Data-Efficient adaptation with Error-Driven learning for code generation.<n> Experimental results show that, compared to other mainstream fine-tuning approaches, DEED achieves superior performance with few training data.
  arXiv  Detail & Related papers  (2024-02-29T16:09:02Z)
• Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes [54.13559879916708]
  EVAPORATE is a prototype system powered by large language models (LLMs)<n>Code synthesis is cheap, but far less accurate than directly processing each document with the LLM.<n>We propose an extended code implementation, EVAPORATE-CODE+, which achieves better quality than direct extraction.
  arXiv  Detail & Related papers  (2023-04-19T06:00:26Z)
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning [92.36705236706678]
  "CodeRL" is a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning. During inference, we introduce a new generation procedure with a critical sampling strategy. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives.
  arXiv  Detail & Related papers  (2022-07-05T02:42:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.