DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model

• URL: http://arxiv.org/abs/2502.15832v1
• Date: Thu, 20 Feb 2025 11:07:55 GMT
• Title: DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model
• Authors: Yi Liu, Changran Xu, Yunhao Zhou, Zeju Li, Qiang Xu,
• Abstract summary: We present DeepRTL, a unified representation model that excels in both Verilog understanding and generation.<n>Based on CodeT5+, DeepRTL is fine-tuned on a comprehensive dataset that aligns Verilog code with rich, multi-level natural language descriptions.<n>We introduce the first benchmark for Verilog understanding and take the initiative to apply embedding similarity and GPT Score to evaluate the models' understanding capabilities.
• Score: 13.532046953850902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in large language models (LLMs) have shown significant potential for automating hardware description language (HDL) code generation from high-level natural language instructions. While fine-tuning has improved LLMs' performance in hardware design tasks, prior efforts have largely focused on Verilog generation, overlooking the equally critical task of Verilog understanding. Furthermore, existing models suffer from weak alignment between natural language descriptions and Verilog code, hindering the generation of high-quality, synthesizable designs. To address these issues, we present DeepRTL, a unified representation model that excels in both Verilog understanding and generation. Based on CodeT5+, DeepRTL is fine-tuned on a comprehensive dataset that aligns Verilog code with rich, multi-level natural language descriptions. We also introduce the first benchmark for Verilog understanding and take the initiative to apply embedding similarity and GPT Score to evaluate the models' understanding capabilities. These metrics capture semantic similarity more accurately than traditional methods like BLEU and ROUGE, which are limited to surface-level n-gram overlaps. By adapting curriculum learning to train DeepRTL, we enable it to significantly outperform GPT-4 in Verilog understanding tasks, while achieving performance on par with OpenAI's o1-preview model in Verilog generation tasks.

Related papers

• Speculative Decoding for Verilog: Speed and Quality, All in One [14.64921497909531]
  We introduce a novel application of speculative decoding for Verilog code generation. Unlike standard tokenization schemes, our approach aligns decoding stops with syntactically significant tokens. Our experimental results show that our method achieves up to a 5.05x speedup in Verilog code generation.
  arXiv  Detail & Related papers  (2025-03-18T11:21:53Z)
• GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model [63.774726052837266]
  We introduce a new architecture that deeply integrates Graph Neural Networks (GNNs) with Large Language Models (LLMs) We introduce three key innovations: (1) Structure-Aware Transformers, which incorporate GNN's message-passing capabilities directly into LLM's transformer layers; (2) Graph-Text Cross-Attention, which processes full, uncompressed text from graph nodes and edges; and (3) GNN-LLM Twin Predictor, enabling LLM's flexible autoregressive generation alongside GNN's scalable one-pass prediction.
  arXiv  Detail & Related papers  (2024-12-08T05:49:58Z)
• How to Make LLMs Strong Node Classifiers? [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs)<n>We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art (SOTA) GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• Large Language Model for Verilog Generation with Code-Structure-Guided Reinforcement Learning [29.135207235743795]
  This paper introduces VeriSeek, an LLM enhanced by reinforcement learning to achieve high Verilog code generation performance. Our reinforcement learning approach employs code structure information as feedback signals to refine the pre-trained model. Experiments show that VeriSeek outperforms state-of-the-art methods across multiple benchmarks.
  arXiv  Detail & Related papers  (2024-07-21T11:25:21Z)
• CodeV: Empowering LLMs for Verilog Generation through Multi-Level Summarization [37.4446786461791]
  This paper introduces CodeV, a series of open-source instruction-tuned Verilog generation LLMs. We show that CodeV relatively surpasses the previous open-source SOTA by 14.4% (BetterV in VerilogEval) and 11.3% (RTLCoder in RTLLM) respectively.
  arXiv  Detail & Related papers  (2024-07-15T03:57:20Z)
• CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
  We propose CodeGRAG, a Graphical Retrieval Augmented Code Generation framework to enhance the performance of LLMs. CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to fill the gap between programming languages and natural language. Various experiments and ablations are done on four datasets including both the C++ and python languages to validate the hard meta-graph prompt, the soft prompting technique, and the effectiveness of the objectives for pretrained GNN expert.
  arXiv  Detail & Related papers  (2024-05-03T02:48:55Z)
• A Multi-Expert Large Language Model Architecture for Verilog Code Generation [5.159745269633967]
  This paper introduces an innovative multi-expert LLM architecture for Verilog code generation (MEV-LLM) Our architecture uniquely integrates multiple LLMs, each specifically fine-tuned with a dataset that is categorized with respect to a distinct level of design complexity. Empirical evidence from experiments highlights notable improvements in terms of the percentage of generated Verilog outputs that are syntactically and functionally correct.
  arXiv  Detail & Related papers  (2024-04-11T16:58:29Z)
• Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework [50.02710905062184]
  This paper proposes an automated design-data augmentation framework, which generates high-volume and high-quality natural language aligned with Verilog and EDA scripts. The accuracy of Verilog generation surpasses that of the current state-of-the-art open-source Verilog generation model, increasing from 58.8% to 70.6% with the same benchmark.
  arXiv  Detail & Related papers  (2024-03-17T13:01:03Z)
• Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation [22.219645213202178]
  This paper proposes the "Semantic Chain-of-Thought" approach to intruduce semantic information of code, named SeCoT. We show that SeCoT can achieves state-of-the-art performance, greatly improving the potential for large models and code generation.
  arXiv  Detail & Related papers  (2023-10-16T05:09:58Z)
• VerilogEval: Evaluating Large Language Models for Verilog Code Generation [6.88526119890374]
  We present a comprehensive evaluation dataset consisting of 156 problems from the Verilog instructional website HDLBits. The evaluation set consists of a diverse set of Verilog code generation tasks, ranging from simple combinational circuits to complex finite state machines.
  arXiv  Detail & Related papers  (2023-09-14T09:15:34Z)
• Instruction Position Matters in Sequence Generation with Large Language Models [67.87516654892343]
  Large language models (LLMs) are capable of performing conditional sequence generation tasks, such as translation or summarization. We propose enhancing the instruction-following capability of LLMs by shifting the position of task instructions after the input sentences.
  arXiv  Detail & Related papers  (2023-08-23T12:36:57Z)
• LeTI: Learning to Generate from Textual Interactions [60.425769582343506]
  We explore LMs' potential to learn from textual interactions (LETI) that not only check their correctness with binary labels but also pinpoint and explain errors in their outputs through textual feedback. Our focus is the code generation task, where the model produces code based on natural language instructions. LETI iteratively fine-tunes the model, using the objective LM, on a concatenation of natural language instructions, LM-generated programs, and textual feedback.
  arXiv  Detail & Related papers  (2023-05-17T15:53:31Z)
• Logical Natural Language Generation from Open-Domain Tables [107.04385677577862]
  We propose a new task where a model is tasked with generating natural language statements that can be emphlogically entailed by the facts. To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset citechen 2019tabfact featured with a wide range of logical/symbolic inferences. The new task poses challenges to the existing monotonic generation frameworks due to the mismatch between sequence order and logical order.
  arXiv  Detail & Related papers  (2020-04-22T06:03:10Z)

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.