{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:20:43Z","timestamp":1776273643929,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,27]],"date-time":"2024-10-27T00:00:00Z","timestamp":1729987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation of China","award":["T2325001"],"award-info":[{"award-number":["T2325001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,27]]},"DOI":"10.1145\/3676536.3676830","type":"proceedings-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:21:20Z","timestamp":1744204880000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["OriGen: Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0132-628X","authenticated-orcid":false,"given":"Fan","family":"Cui","sequence":"first","affiliation":[{"name":"School of Integrated Circuits, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6068-7223","authenticated-orcid":false,"given":"Chenyang","family":"Yin","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7635-3425","authenticated-orcid":false,"given":"Kexing","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3636-3685","authenticated-orcid":false,"given":"Youwei","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7315-6589","authenticated-orcid":false,"given":"Guangyu","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6747-126X","authenticated-orcid":false,"given":"Qiang","family":"Xu","sequence":"additional","affiliation":[{"name":"the Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8805-8789","authenticated-orcid":false,"given":"Qipeng","family":"Guo","sequence":"additional","affiliation":[{"name":"Shanghai AI Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9076-7998","authenticated-orcid":false,"given":"Yun","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8525-0608","authenticated-orcid":false,"given":"Xingcheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai AI Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8988-461X","authenticated-orcid":false,"given":"Demin","family":"Song","sequence":"additional","affiliation":[{"name":"Shanghai AI Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8865-7896","authenticated-orcid":false,"given":"Dahua","family":"Lin","sequence":"additional","affiliation":[{"name":"Shanghai AI Laboratory, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_1_2_1","unstructured":"Anthropic. 2024. Introducing the next generation of Claude. https:\/\/www.anthropic.com\/news\/claude-3-family"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLCAD58807.2023.10299874"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1950413.1950423"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Kaiyan Chang Kun Wang Nan Yang Ying Wang Dantong Jin Wenlong Zhu Zhirong Chen Cangyuan Li Hao Yan Yunhao Zhou et al. 2024. Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework. arXiv preprint arXiv:2403.11202 (2024).","DOI":"10.1145\/3649329.3657356"},{"key":"e_1_3_2_1_7_1","volume-title":"Chipgpt: How far are we from natural language hardware design. arXiv preprint arXiv:2305.14019","author":"Chang Kaiyan","year":"2023","unstructured":"Kaiyan Chang, Ying Wang, Haimeng Ren, Mengdi Wang, Shengwen Liang, Yinhe Han, Huawei Li, and Xiaowei Li. 2023. Chipgpt: How far are we from natural language hardware design. arXiv preprint arXiv:2305.14019 (2023)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDT.2009.69"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317842"},{"key":"e_1_3_2_1_10_1","volume-title":"A deep learning framework for verilog autocompletion towards design and verification automation. arXiv preprint arXiv:2304.13840","author":"Dehaerne Enrique","year":"2023","unstructured":"Enrique Dehaerne, Bappaditya Dey, Sandip Halder, and Stefan De Gendt. 2023. A deep learning framework for verilog autocompletion towards design and verification automation. arXiv preprint arXiv:2304.13840 (2023)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD57390.2023.10323953"},{"key":"e_1_3_2_1_12_1","unstructured":"Daya Guo Qihao Zhu Dejian Yang Zhenda Xie Kai Dong Wentao Zhang Guanting Chen Xiao Bi Y Wu YK Li et al. 2024. DeepSeek-Coder: When the Large Language Model Meets Programming-The Rise of Code Intelligence. arXiv preprint arXiv:2401.14196 (2024)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317924"},{"key":"e_1_3_2_1_14_1","volume-title":"Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685","author":"Hu Edward J","year":"2021","unstructured":"Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18074.2021.9586329"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3489517.3530411"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3174243.3174264"},{"key":"e_1_3_2_1_18_1","volume-title":"Ammus: A survey of transformer-based pretrained models in natural language processing. arXiv preprint arXiv:2108.05542","author":"Kalyan Katikapalli Subramanyam","year":"2021","unstructured":"Katikapalli Subramanyam Kalyan, Ajit Rajasekharan, and Sivanesan Sangeetha. 2021. Ammus: A survey of transformer-based pretrained models in natural language processing. arXiv preprint arXiv:2108.05542 (2021)."},{"key":"e_1_3_2_1_19_1","volume-title":"Chipnemo: Domain-adapted llms for chip design. arXiv preprint arXiv:2311.00176","author":"Liu Mingjie","year":"2023","unstructured":"Mingjie Liu, Teodor-Dumitru Ene, Robert Kirby, Chris Cheng, Nathaniel Pinckney, Rongjian Liang, Jonah Alben, Himyanshu Anand, Sanmitra Banerjee, Ismet Bayraktaroglu, et al. 2023. Chipnemo: Domain-adapted llms for chip design. arXiv preprint arXiv:2311.00176 (2023)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD57390.2023.10323812"},{"key":"e_1_3_2_1_21_1","volume-title":"Rtlcoder: Outperforming gpt-3.5 in design rtl generation with our open-source dataset and lightweight solution. arXiv preprint arXiv:2312.08617","author":"Liu Shang","year":"2023","unstructured":"Shang Liu, Wenji Fang, Yao Lu, Qijun Zhang, Hongce Zhang, and Zhiyao Xie. 2023. Rtlcoder: Outperforming gpt-3.5 in design rtl generation with our open-source dataset and lightweight solution. arXiv preprint arXiv:2312.08617 (2023)."},{"key":"e_1_3_2_1_22_1","volume-title":"Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, et al.","author":"Lozhkov Anton","year":"2024","unstructured":"Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, et al. 2024. StarCoder 2 and The Stack v2: The Next Generation. arXiv preprint arXiv:2402.19173 (2024)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASP-DAC58780.2024.10473904"},{"key":"e_1_3_2_1_24_1","volume-title":"A Multi-Expert Large Language Model Architecture for Verilog Code Generation. arXiv preprint arXiv:2404.08029","author":"Nadimi Bardia","year":"2024","unstructured":"Bardia Nadimi and Hao Zheng. 2024. A Multi-Expert Large Language Model Architecture for Verilog Code Generation. arXiv preprint arXiv:2404.08029 (2024)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3380446.3430634"},{"key":"e_1_3_2_1_26_1","volume-title":"BetterV: Controlled Verilog Generation with Discriminative Guidance. arXiv preprint arXiv:2402.03375","author":"Pei Zehua","year":"2024","unstructured":"Zehua Pei, Hui-Ling Zhen, Mingxuan Yuan, Yu Huang, and Bei Yu. 2024. BetterV: Controlled Verilog Generation with Discriminative Guidance. arXiv preprint arXiv:2402.03375 (2024)."},{"key":"e_1_3_2_1_27_1","volume-title":"Yossi Adi, Jingyu Liu, Tal Remez, J\u00e9r\u00e9my Rapin, et al.","author":"Roziere Baptiste","year":"2023","unstructured":"Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, J\u00e9r\u00e9my Rapin, et al. 2023. Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950 (2023)."},{"key":"e_1_3_2_1_28_1","unstructured":"Qiushi Sun Zhirui Chen Fangzhi Xu Kanzhi Cheng Chang Ma Zhangyue Yin Jianing Wang Chengcheng Han Renyu Zhu Shuai Yuan Qipeng Guo Xipeng Qiu Pengcheng Yin Xiaoli Li Fei Yuan Lingpeng Kong Xiang Li and Zhiyong Wu. 2024. A Survey of Neural Code Intelligence: Paradigms Advances and Beyond. arXiv:2403.14734"},{"key":"e_1_3_2_1_29_1","volume-title":"Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1--6.","author":"Thakur Shailja","year":"2023","unstructured":"Shailja Thakur, Baleegh Ahmad, Zhenxing Fan, Hammond Pearce, Benjamin Tan, Ramesh Karri, Brendan Dolan-Gavitt, and Siddharth Garg. 2023. Benchmarking large language models for automated verilog rtl code generation. In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1--6."},{"key":"e_1_3_2_1_30_1","volume-title":"Verigen: A large language model for verilog code generation. ACM Transactions on Design Automation of Electronic Systems","author":"Thakur Shailja","year":"2023","unstructured":"Shailja Thakur, Baleegh Ahmad, Hammond Pearce, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri, and Siddharth Garg. 2023. Verigen: A large language model for verilog code generation. ACM Transactions on Design Automation of Electronic Systems (2023)."},{"key":"e_1_3_2_1_31_1","volume-title":"Autochip: Automating hdl generation using llm feedback. arXiv preprint arXiv:2311.04887","author":"Thakur Shailja","year":"2023","unstructured":"Shailja Thakur, Jason Blocklove, Hammond Pearce, Benjamin Tan, Siddharth Garg, and Ramesh Karri. 2023. Autochip: Automating hdl generation using llm feedback. arXiv preprint arXiv:2311.04887 (2023)."},{"key":"e_1_3_2_1_32_1","volume-title":"Rtlfixer: Automatically fixing rtl syntax errors with large language models. arXiv preprint arXiv:2311.16543","author":"Tsai YunDa","year":"2023","unstructured":"YunDa Tsai, Mingjie Liu, and Haoxing Ren. 2023. Rtlfixer: Automatically fixing rtl syntax errors with large language models. arXiv preprint arXiv:2311.16543 (2023)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/513581.513584"},{"key":"e_1_3_2_1_34_1","volume-title":"Chateda: A large language model powered autonomous agent for eda","author":"Wu Haoyuan","year":"2024","unstructured":"Haoyuan Wu, Zhuolun He, Xinyun Zhang, Xufeng Yao, Su Zheng, Haisheng Zheng, and Bei Yu. 2024. Chateda: A large language model powered autonomous agent for eda. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2024)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3508352.3549370"},{"key":"e_1_3_2_1_36_1","unstructured":"John Yang Carlos E. Jimenez Alexander Wettig Kilian Lieret Shunyu Yao Karthik Narasimhan and Ofir Press. 2024. SWE-agent: Agent Computer Interfaces Enable Software Engineering Language Models."}],"event":{"name":"ICCAD '24: 43rd IEEE\/ACM International Conference on Computer-Aided Design","location":"Newark Liberty International Airport Marriott New York NY USA","acronym":"ICCAD '24","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CAS","IEEE CEDA","IEEE EDS"]},"container-title":["Proceedings of the 43rd IEEE\/ACM International Conference on Computer-Aided Design"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3676536.3676830","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3676536.3676830","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:44Z","timestamp":1750295924000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3676536.3676830"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,27]]},"references-count":35,"alternative-id":["10.1145\/3676536.3676830","10.1145\/3676536"],"URL":"https:\/\/doi.org\/10.1145\/3676536.3676830","relation":{},"subject":[],"published":{"date-parts":[[2024,10,27]]},"assertion":[{"value":"2025-04-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}