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DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis

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arxiv 2502.18297 v1 pith:CE3WEWOJ submitted 2025-02-25 cs.LG cs.PL

DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis

Zeju Li , Changran Xu , Zhengyuan Shi , Zedong Peng , Yi Liu , Yunhao Zhou , Lingfeng Zhou , Chengyu Ma
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Jianyuan Zhong Xi Wang Jieru Zhao Zhufei Chu Xiaoyan Yang Qiang Xu
This is my paper
classification cs.LG cs.PL
keywords codedatasetdeepcircuitxgenerationunderstandinganalysisannotationscomprehensive
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This paper introduces DeepCircuitX, a comprehensive repository-level dataset designed to advance RTL (Register Transfer Level) code understanding, generation, and power-performance-area (PPA) analysis. Unlike existing datasets that are limited to either file-level RTL code or physical layout data, DeepCircuitX provides a holistic, multilevel resource that spans repository, file, module, and block-level RTL code. This structure enables more nuanced training and evaluation of large language models (LLMs) for RTL-specific tasks. DeepCircuitX is enriched with Chain of Thought (CoT) annotations, offering detailed descriptions of functionality and structure at multiple levels. These annotations enhance its utility for a wide range of tasks, including RTL code understanding, generation, and completion. Additionally, the dataset includes synthesized netlists and PPA metrics, facilitating early-stage design exploration and enabling accurate PPA prediction directly from RTL code. We demonstrate the dataset's effectiveness on various LLMs finetuned with our dataset and confirm the quality with human evaluations. Our results highlight DeepCircuitX as a critical resource for advancing RTL-focused machine learning applications in hardware design automation.Our data is available at https://zeju.gitbook.io/lcm-team.

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Cited by 3 Pith papers

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  3. PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents

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    A multi-agent repair framework that samples multiple edit locations and iteratively reflects on patch attempts reaches 76.0% Pass@1 on SWE-bench-Verified, up to a 7.8% relative gain over SWE-agent.