{"677911":{"#nid":"677911","#data":{"type":"news","title":"ECE Research Group Develops Open-Source Infrastructure to Advance Machine Learning for Hardware Design","body":[{"value":"\u003Cp\u003EA research group from the \u003Ca href=\u0022https:\/\/ece.gatech.edu\/\u0022\u003ESchool of Electrical and Computer Engineering\u003C\/a\u003E (ECE), led by Assistant Professor \u003Ca href=\u0022https:\/\/ece.gatech.edu\/directory\/callie-hao\u0022\u003ECallie Hao\u003C\/a\u003E, won the Best Paper Award at the 2024 ACM\/IEEE International Symposium on Machine Learning for CAD, held in Salt Lake City, Utah, from Sept. 9\u201311.\u003C\/p\u003E\u003Cp\u003EThe award-winning paper, \u201c\u003Ca href=\u0022https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3670474.3685961\u0022\u003EHLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond,\u003C\/a\u003E\u201d introduces an open-source infrastructure that simplifies the contribution and sharing of hardware designs.\u003C\/p\u003E\u003Cp\u003EThis platform allows developers to submit their designs to a common pool, making it easy for machine learning practitioners to access the data they need to train their algorithms, ultimately driving advancements in hardware design.\u003C\/p\u003E\u003Cp\u003EThe framework addresses a major challenge in applying machine learning to chip design:the scarcity of high-quality training data. By being open-source, HLSFactory encourages a wide range of contributions from the community, creating a richer dataset for researchers and developers.\u003C\/p\u003E\u003Cp\u003E\u201cHLSFactory creates unified platform that the entire community can leverage to drive more scientific discoveries and improve design outcomes,\u201d said Hao.\u003C\/p\u003E\u003Cp\u003EThe software was developed through Hao\u0027s \u003Ca href=\u0022https:\/\/sharclab.ece.gatech.edu\/\u0022\u003ESoftware\/Hardware Co-Design Lab\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003EECE Ph.D. candidate Stefan Abi-Karam was the lead author on the paper. Abi-Karam previously won the Community Award at the 33rd International Conference on Field-Programmable Logic and Applications (FPL) 2023, for his work on \u201cGNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization.\u201d The award recognized his contributions to the open-source community, underscoringthe group\u2019s dedication to open-source tools and hardware design, according to Hao.\u003C\/p\u003E\u003Cp\u003EECE Ph.D. candidates Rishov Sarkar and Hanqiu Chen also contributed to the paper, alongside collaborators Allison Seigler, Sean Lowe, Zhigang Wei, Nanditha Rao, and Lizy Kurian John from the University of Texas at Austin, and Aman Arora from Arizona State University.\u003C\/p\u003E\u003Cp\u003EHao, who \u003Ca href=\u0022https:\/\/ece.gatech.edu\/news\/2023\/12\/hao-joins-ece-faculty-appointed-sutterfield-professorship\u0022\u003Ejoined the Georgia Tech ECE faculty in 2022\u003C\/a\u003E, specializes in efficient hardware design and machine learning algorithms. Her research focuses on reconfigurable, high-efficiency computing and developing practical electronic design automation tools. She has previously been recognized with several prestigious awards, including the \u003Ca href=\u0022https:\/\/ece.gatech.edu\/news\/2024\/03\/hao-wins-nsf-career-award-digital-hardware-design-research-0\u0022\u003ENSF CAREER Award\u003C\/a\u003E and the \u003Ca href=\u0022https:\/\/ece.gatech.edu\/news\/2023\/12\/hao-earns-intel-rising-star-faculty-award\u0022\u003EIntel Rising Star Faculty Award\u003C\/a\u003E, for her groundbreaking work in hardware design research.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003ELed by Assistant Professor Cong \u0022Callie\u0022 Hao, the group is creating an open-source platform that enables hardware developers to contribute designs for use by machine learning practitioners.\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Led by Assistant Professor Cong \u0022Callie\u0022 Hao, the group is creating an open-source platform that enables hardware developers to contribute designs for use by machine learning practitioners."}],"uid":"36558","created_gmt":"2024-10-24 20:02:03","changed_gmt":"2024-10-24 21:09:22","author":"zwiniecki3","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2024-10-24T00:00:00-04:00","iso_date":"2024-10-24T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"675434":{"id":"675434","type":"image","title":"Hao Best Paper.jpg","body":null,"created":"1729800149","gmt_created":"2024-10-24 20:02:29","changed":"1729800149","gmt_changed":"2024-10-24 20:02:29","alt":"Callie Hao and ECE Ph.D. candidate Stefan Abi-Karam with the Best Paper Award.","file":{"fid":"259056","name":"Hao Best Paper.jpg","image_path":"\/sites\/default\/files\/2024\/10\/24\/Hao%20Best%20Paper.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/10\/24\/Hao%20Best%20Paper.jpg","mime":"image\/jpeg","size":1430551,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/10\/24\/Hao%20Best%20Paper.jpg?itok=CpjGCzmr"}}},"media_ids":["675434"],"groups":[{"id":"1255","name":"School of Electrical and Computer Engineering"}],"categories":[{"id":"145","name":"Engineering"}],"keywords":[{"id":"103141","name":"Best Paper Award"},{"id":"9167","name":"machine learning"},{"id":"193045","name":"Software\/Hardware Co-design Lab"}],"core_research_areas":[{"id":"193655","name":"Artificial Intelligence at Georgia Tech"},{"id":"39451","name":"Electronics and Nanotechnology"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EZachary Winiecki\u003C\/p\u003E","format":"limited_html"}],"email":["zwiniecki3@gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}