{"677306":{"#nid":"677306","#data":{"type":"event","title":"PhD Proposal by Yongan (Luke) Zhang","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle: Harnessing Large Language Models Towards Customized Hardware Design Automation\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EYongan (Luke) Zhang\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EPh.D. Student\u003C\/p\u003E\u003Cp\u003ESchool of Computer Science, Colleague of Computing\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/luke-avionics.github.io\/\u0022\u003Ehttps:\/\/luke-avionics.github.io\/\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E\u0026nbsp;Thursday, October\u0026nbsp;10th, 2024\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E\u0026nbsp;3:00 PM - 5:00 PM EDT\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation (in-person)\u003C\/strong\u003E:\u0026nbsp;Klaus Advanced Computing Building - 3100\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EZoom\u003C\/strong\u003E: \u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/6669781434?pwd=OG4xZk1XakJyUWVTRWJIdEFveUFNQT09\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/6669781434?pwd=OG4xZk1XakJyUWVTRWJIdEFveUFNQT09\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Yingyan\u0026nbsp;(Celine) Lin (Advisor), College of Computing, Georgia Institute of Technology\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Hyesoon Kim, College of Computing, Georgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Haoxing (Mark) Ren, Design Automation Research, Nvidia\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u003C\/p\u003E\u003Cp\u003EThe rapid advancement of Artificial Intelligence (AI) has created a growing demand for customized hardware accelerators. However, the design process for these accelerators is time-consuming and requires extensive hardware expertise, hindering the adoption of customized hardware for many applications. My thesis research aims to democratize customized hardware design by leveraging large language models (LLMs) to automate the process. I propose a set of pioneering domain-specific adaptation techniques that enable LLMs to tackle hardware design tasks more effectively. These techniques include in-context learning flows with automatically retrieved hardware design demonstrations, an automated framework for generating high-quality hardware datasets to fine-tune specialized LLM, and LLM-friendly problem decomposition techniques for tackling complex hardware design flows. To support the practical implementation of LLMs in hardware design, I have also organized community efforts to create infrastructures that assist relevant research on LLMs for hardware design. Furthermore, I dedicate ongoing efforts to apply my proposed hardware domain-specific adaptation techniques to enhance LLMs\u0027 reasoning capabilities, enabling them to tackle more challenging and practical hardware design problems across different stages of the design flow, akin to a human designer. By harnessing the power of LLMs and developing domain-specific adaptation techniques, my thesis research aims to revolutionize the customized hardware design process, making it more accessible and efficient for a wider range of developers and applications.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EHarnessing Large Language Models Towards Customized Hardware Design Automation\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Harnessing Large Language Models Towards Customized Hardware Design Automation"}],"uid":"27707","created_gmt":"2024-10-03 18:02:14","changed_gmt":"2024-10-03 18:02:14","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-10-10T15:00:00-04:00","event_time_end":"2024-10-10T17:00:08-04:00","event_time_end_last":"2024-10-10T17:00:08-04:00","gmt_time_start":"2024-10-10 19:00:00","gmt_time_end":"2024-10-10 21:00:08","gmt_time_end_last":"2024-10-10 21:00:08","rrule":null,"timezone":"America\/New_York"},"location":"Klaus Advanced Computing Building - 3100","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}