RealBench: Benchmarking Verilog Generation Models with Real-World IP Designs

• URL: http://arxiv.org/abs/2507.16200v1
• Date: Tue, 22 Jul 2025 03:29:23 GMT
• Title: RealBench: Benchmarking Verilog Generation Models with Real-World IP Designs
• Authors: Pengwei Jin, Di Huang, Chongxiao Li, Shuyao Cheng, Yang Zhao, Xinyao Zheng, Jiaguo Zhu, Shuyi Xing, Bohan Dou, Rui Zhang, Zidong Du, Qi Guo, Xing Hu,
• Abstract summary: We present RealBench, the first benchmark aiming at real-world IP-level Verilog generation tasks.<n>RealBench features complex, structured, real-world open-source IP designs, multi-modal and formatted design specifications, and rigorous verification environments.<n> Evaluations on various LLMs and agents reveal that even one of the best-performing LLMs, o1-preview, achieves only a 13.3% pass@1 on module-level tasks and 0% on system-level tasks.
• Score: 26.993718615404926
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The automatic generation of Verilog code using Large Language Models (LLMs) has garnered significant interest in hardware design automation. However, existing benchmarks for evaluating LLMs in Verilog generation fall short in replicating real-world design workflows due to their designs' simplicity, inadequate design specifications, and less rigorous verification environments. To address these limitations, we present RealBench, the first benchmark aiming at real-world IP-level Verilog generation tasks. RealBench features complex, structured, real-world open-source IP designs, multi-modal and formatted design specifications, and rigorous verification environments, including 100% line coverage testbenches and a formal checker. It supports both module-level and system-level tasks, enabling comprehensive assessments of LLM capabilities. Evaluations on various LLMs and agents reveal that even one of the best-performing LLMs, o1-preview, achieves only a 13.3% pass@1 on module-level tasks and 0% on system-level tasks, highlighting the need for stronger Verilog generation models in the future. The benchmark is open-sourced at https://github.com/IPRC-DIP/RealBench.

Related papers

• ChipBench: A Next-Step Benchmark for Evaluating LLM Performance in AI-Aided Chip Design [15.71144418188142]
  Large Language Models (LLMs) show significant potential in hardware engineering.<n>Current benchmarks suffer from saturation and limited task diversity.<n>We propose a comprehensive benchmark for AI-aided chip design.
  arXiv  Detail & Related papers  (2026-01-29T09:26:55Z)
• ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development [72.4729759618632]
  We introduce ABC-Bench, a benchmark to evaluate agentic backend coding within a realistic, executable workflow.<n>We curated 224 practical tasks spanning 8 languages and 19 frameworks from open-source repositories.<n>Our evaluation reveals that even state-of-the-art models struggle to deliver reliable performance on these holistic tasks.
  arXiv  Detail & Related papers  (2026-01-16T08:23:52Z)
• Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers [103.4410890572479]
  We introduce the Loong Project: an open-source framework for scalable synthetic data generation and verification.<n>LoongBench is a curated seed dataset containing 8,729 human-vetted examples across 12 domains.<n>LoongEnv is a modular synthetic data generation environment that supports multiple prompting strategies to produce new question-answer-code triples.
  arXiv  Detail & Related papers  (2025-09-03T06:42:40Z)
• InternBootcamp Technical Report: Boosting LLM Reasoning with Verifiable Task Scaling [71.37579508777843]
  Large language models (LLMs) have revolutionized artificial intelligence by enabling complex reasoning capabilities.<n>To address this gap, we present InternBootcamp, an open-source framework comprising 1000+ domain-diverse task environments.
  arXiv  Detail & Related papers  (2025-08-12T05:00:00Z)
• Dynamic Benchmark Construction for Evaluating Large Language Models on Real-World Codes [33.80591142965565]
  We present CODE2BENCH, a pipeline for dynamically constructing robust and contamination-resistant benchmarks from real-world GitHub repositories.<n>Specifically, CODE2BENCH introduces three key innovations: (1) Automated Dynamism, achieved through periodic ingestion of recent code to minimize training data contamination; (2) Scope Graph-based dependency analysis, which enables structured classification of functions into benchmark instances with controlled dependency levels; and (3) Property-Based Testing (PBT) for the automated synthesis of rigorous test suites.
  arXiv  Detail & Related papers  (2025-08-10T05:06:36Z)
• Evaluating Large Language Models on Non-Code Software Engineering Tasks [4.381476817430934]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in code understanding and generation.<n>We present the first comprehensive benchmark, which we name Software Engineering Language Understanding' (SELU)<n>SELU covers classification, regression, Named Entity Recognition (NER) and Masked Language Modeling (MLM) targets, with data drawn from diverse sources.
  arXiv  Detail & Related papers  (2025-06-12T15:52:32Z)
• Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition [95.54406667705999]
  Pangu Embedded is an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs)<n>It addresses the significant computational costs and inference latency challenges prevalent in existing reasoning-optimized LLMs.<n>It delivers rapid responses and state-of-the-art reasoning quality within a single, unified model architecture.
  arXiv  Detail & Related papers  (2025-05-28T14:03:02Z)
• OSS-Bench: Benchmark Generator for Coding LLMs [4.393587297483245]
  We introduce OSS-Bench, a benchmark generator that constructs large-scale, live evaluation tasks from real-world open-source software.<n> OSS-Bench replaces functions with LLM-generated code and evaluates them using three natural metrics: compilability, functional correctness, and memory safety.<n>Our results demonstrate that OSS-Bench mitigates overfitting by leveraging the evolving complexity of OSS.
  arXiv  Detail & Related papers  (2025-05-18T09:53:51Z)
• IPBench: Benchmarking the Knowledge of Large Language Models in Intellectual Property [53.2129505804405]
  IPBench is the first comprehensive IP task taxonomy and a large-scale benchmark encompassing 8 IP mechanisms and 20 distinct tasks.<n>We benchmark 17 main LLMs, ranging from general purpose to domain-specific, including chat-oriented and reasoning-focused models.<n>Our results show that even the top-performing model, DeepSeek-V3, achieves only 75.8% accuracy, indicating significant room for improvement.
  arXiv  Detail & Related papers  (2025-04-22T02:00:41Z)
• HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Tasks [4.71707720395444]
  We introduce HLS-Eval, the first complete benchmark and evaluation framework for HLS-driven design.<n>The benchmark includes 94 unique designs drawn from standard HLS benchmarks and novel sources.<n>Beyond the benchmark, HLS-Eval offers a modular Python framework for automated, parallel evaluation of both local and hosted LLMs.
  arXiv  Detail & Related papers  (2025-04-16T17:30:36Z)
• TuRTLe: A Unified Evaluation of LLMs for RTL Generation [0.6010802600885173]
  We propose TuRTLe, a unified evaluation framework designed to assess LLMs across key RTL generation tasks.<n>We benchmark a diverse set of open LLMs and analyze their strengths and weaknesses in EDA-specific tasks.<n>Our results show that reasoning-based models, such as DeepSeek R1, consistently outperform others across multiple evaluation criteria.
  arXiv  Detail & Related papers  (2025-03-31T07:43:12Z)
• VeriMind: Agentic LLM for Automated Verilog Generation with a Novel Evaluation Metric [4.590930025882158]
  We propose VeriMind, an agentic LLM framework for Verilog code generation.<n>We introduce a novel evaluation metric-pass@ARC-which combines the conventional pass@k measure with Average Refinement Cycles (ARC) to capture both success rate and the efficiency of iterative refinement.<n> Experimental results on diverse hardware design tasks demonstrated that our approach achieved up to $8.3%$ improvement on pass@k metric and $8.1%$ on pass@ARC metric.
  arXiv  Detail & Related papers  (2025-03-15T23:43:06Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study [72.24266814625685]
  We explore the performance of large language models (LLMs) across the entire software development lifecycle with DevEval.<n>DevEval features four programming languages, multiple domains, high-quality data collection, and carefully designed and verified metrics for each task.<n> Empirical studies show that current LLMs, including GPT-4, fail to solve the challenges presented within DevEval.
  arXiv  Detail & Related papers  (2024-03-13T15:13:44Z)
• Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering [74.99736967448423]
  We construct Design2Code - the first real-world benchmark for this task.<n>We manually curate 484 diverse real-world webpages as test cases and develop a set of automatic evaluation metrics.<n>Our fine-grained break-down metrics indicate that models mostly lag in recalling visual elements from the input webpages and generating correct layout designs.
  arXiv  Detail & Related papers  (2024-03-05T17:56:27Z)
• MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property [51.43412400869531]
  Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. We contribute a new benchmark, the first Multilingual-oriented quiZ on Intellectual Property (MoZIP), for the evaluation of LLMs in the IP domain. We also develop a new IP-oriented multilingual large language model (called MoZi), which is a BLOOMZ-based model that has been supervised fine-tuned with multilingual IP-related text data.
  arXiv  Detail & Related papers  (2024-02-26T08:27:50Z)
• SEED-Bench-2: Benchmarking Multimodal Large Language Models [67.28089415198338]
  Multimodal large language models (MLLMs) have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs. SEED-Bench-2 comprises 24K multiple-choice questions with accurate human annotations, which spans 27 dimensions. We evaluate the performance of 23 prominent open-source MLLMs and summarize valuable observations.
  arXiv  Detail & Related papers  (2023-11-28T05:53:55Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)

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.