{"656138":{"#nid":"656138","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Anthony Agnesina","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003EElectronic Design Automation for High-Performance and Reliable 3D Memory Cubes and Processors\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Sung-Kyu Lim, ECE, Chair, Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Shimeng Yu, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Tushar Krishna, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Madhavan Swaminathan, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Hyesoon Kim, CoC\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003EThis dissertation explores various techniques for the electronic design automation (EDA) of integrated circuits (IC) with high-performance and reliability features. We propose novel architectures, machine learning techniques, and physical design methodologies to improve the state-of-the-art technology. In the first theme, we conceive a new die stacking architecture for 3D memory cubes targeting space applications, complemented with custom radiation-hardened-by-design logic controllers. In the second theme, we explore machine learning to improve the implementation flow of a large field-programmable gate array (FPGA) emulation system and help tune the many knobs of a very-large-scale integration (VLSI) placement engine. Finally, for the third theme, we present a power, performance, area, and cost (PPAC) analysis of large-scale 3D IC processor designs, motivating our development of a new hierarchical 3D EDA flow focusing on a more holistic silicon area utilization.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Electronic Design Automation for High-Performance and Reliable 3D Memory Cubes and Processors "}],"uid":"28475","created_gmt":"2022-03-08 21:31:50","changed_gmt":"2022-03-08 21:31:50","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2022-03-23T16:30:00-04:00","event_time_end":"2022-03-23T18:30:00-04:00","event_time_end_last":"2022-03-23T18:30:00-04:00","gmt_time_start":"2022-03-23 20:30:00","gmt_time_end":"2022-03-23 22:30:00","gmt_time_end_last":"2022-03-23 22:30:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"},{"id":"1808","name":"graduate students"}],"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":""}}}