{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:19:02Z","timestamp":1774365542813,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,9,9]]},"DOI":"10.1145\/3670474.3685964","type":"proceedings-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T06:22:27Z","timestamp":1725344547000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Rome was Not Built in a Single Step: Hierarchical Prompting for LLM-based Chip Design"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1345-5444","authenticated-orcid":false,"given":"Andre","family":"Nakkab","sequence":"first","affiliation":[{"name":"New York University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4815-9235","authenticated-orcid":false,"given":"Sai Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"New York University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7989-5617","authenticated-orcid":false,"given":"Ramesh","family":"Karri","sequence":"additional","affiliation":[{"name":"New York University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6158-9512","authenticated-orcid":false,"given":"Siddharth","family":"Garg","sequence":"additional","affiliation":[{"name":"New York University"}]}],"member":"320","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"AI@Meta. 2024. Llama 3 Model Card. (2024). https:\/\/github.com\/meta-llama\/llama3\/blob\/main\/MODEL_CARD.md"},{"key":"e_1_3_2_1_2_1","volume-title":"Chip-Chat: Challenges and Opportunities in Conversational Hardware Design. arXiv preprint arXiv:2305.13243","author":"Blocklove Jason","year":"2023","unstructured":"Jason Blocklove, Siddharth Garg, Ramesh Karri, and Hammond Pearce. 2023. Chip-Chat: Challenges and Opportunities in Conversational Hardware Design. arXiv preprint arXiv:2305.13243 (2023)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-08-045352-1.00012-4"},{"key":"e_1_3_2_1_4_1","volume-title":"Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al.","author":"Chen Mark","year":"2021","unstructured":"Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021)."},{"key":"e_1_3_2_1_5_1","unstructured":"S. Cheng P. Jin Q. Guo Z. Du R. Zhang Y. Tian and Y. Chen. 2023. Pushing the Limits of Machine Design: Automated CPU Design with AI. arXiv preprint arXiv:2306.12456 (2023)."},{"key":"e_1_3_2_1_6_1","volume-title":"Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155","author":"Feng Zhangyin","year":"2020","unstructured":"Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al. 2020. Codebert: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155 (2020)."},{"key":"e_1_3_2_1_7_1","volume-title":"Incoder: A generative model for code infilling and synthesis. arXiv preprint arXiv:2204.05999","author":"Fried Daniel","year":"2022","unstructured":"Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022. Incoder: A generative model for code infilling and synthesis. arXiv preprint arXiv:2204.05999 (2022)."},{"key":"e_1_3_2_1_8_1","unstructured":"X. Jiang Y. Dong L. Wang Q. Shang and G. Li. 2023. Self-planning code generation with large language model. arXiv preprint arXiv:2303.06689 (2023)."},{"key":"e_1_3_2_1_9_1","unstructured":"Fang Liu Yang Liu Lin Shi Houkun Huang Ruifeng Wang Zhen Yang Li Zhang Zhongqi Li and Yuchi Ma. 2024. Exploring and Evaluating Hallucinations in LLM-Powered Code Generation. arXiv:2404.00971 [cs.SE]"},{"key":"e_1_3_2_1_10_1","unstructured":"Mingjie Liu Nathaniel Pinckney Brucek Khailany and Haoxing Ren. 2023. VerilogEval: Evaluating Large Language Models for Verilog Code Generation. arXiv:2309.07544 [cs.LG]"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Shang Liu Wenji Fang Yao Lu Qijun Zhang Hongce Zhang and Zhiyao Xie. 2024. RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution. arXiv:2312.08617 [cs.PL]","DOI":"10.1109\/LAD62341.2024.10691788"},{"key":"e_1_3_2_1_12_1","volume-title":"RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model. arXiv:2308.05345 [cs.LG]","author":"Lu Yao","year":"2023","unstructured":"Yao Lu, Shang Liu, Qijun Zhang, and Zhiyao Xie. 2023. RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model. arXiv:2308.05345 [cs.LG]"},{"key":"e_1_3_2_1_13_1","unstructured":"James Manyika. 2023. An overview of Bard: an early experiment with generative AI. Technical Report. Technical report Google AI."},{"key":"e_1_3_2_1_14_1","volume-title":"Codegen: An open large language model for code with multi-turn program synthesis. arXiv preprint arXiv:2203.13474","author":"Nijkamp Erik","year":"2022","unstructured":"Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022. Codegen: An open large language model for code with multi-turn program synthesis. arXiv preprint arXiv:2203.13474 (2022)."},{"key":"e_1_3_2_1_15_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. http:\/\/arxiv.org\/abs\/2303.08774. https:\/\/doi.org\/10.48550\/arXiv.2303.08774 arXiv:2303.08774 [cs]."},{"key":"e_1_3_2_1_16_1","volume-title":"Yossi Adi, Jingyu Liu, Tal Remez, J\u00e9r\u00e9my Rapin, et al.","author":"Rozi\u00e8re Baptiste","year":"2023","unstructured":"Baptiste Rozi\u00e8re, 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_17_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_18_1","volume-title":"VeriGen: A Large Language Model for Verilog Code Generation. arXiv preprint arXiv:2308.00708","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. arXiv preprint arXiv:2308.00708 (2023)."},{"key":"e_1_3_2_1_19_1","unstructured":"Shailja Thakur Jason Blocklove Hammond Pearce Benjamin Tan Siddharth Garg and Ramesh Karri. 2023. AutoChip: Automating HDL Generation Using LLM Feedback. arXiv:2311.04887 [cs.PL]"},{"key":"e_1_3_2_1_20_1","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale et al. 2023. Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv preprint arXiv:2307.09288 (2023)."},{"key":"e_1_3_2_1_21_1","volume-title":"Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. arXiv preprint arXiv:2109.00859","author":"Wang Yue","year":"2021","unstructured":"Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021. Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. arXiv preprint arXiv:2109.00859 (2021)."},{"key":"e_1_3_2_1_22_1","volume-title":"Chi, Quoc Le, and Denny Zhou","author":"Wei Jason","year":"2023","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv:2201.11903 [cs.CL]"}],"event":{"name":"MLCAD '24: 2024 ACM\/IEEE International Symposium on Machine Learning for CAD","location":"Salt Lake City UT USA","acronym":"MLCAD '24","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CEDA"]},"container-title":["Proceedings of the 2024 ACM\/IEEE International Symposium on Machine Learning for CAD"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3670474.3685964","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3670474.3685964","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T23:42:59Z","timestamp":1755906179000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3670474.3685964"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":22,"alternative-id":["10.1145\/3670474.3685964","10.1145\/3670474"],"URL":"https:\/\/doi.org\/10.1145\/3670474.3685964","relation":{},"subject":[],"published":{"date-parts":[[2024,9,9]]},"assertion":[{"value":"2024-09-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}