{"691076":{"#nid":"691076","#data":{"type":"event","title":"Ph.D. Dissertation Defense - James Read","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; Compute-in-Memory for Efficient AI: Cross-Layer Modeling and Hardware-Software Co-Design\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Shimeng Yu, ECE, Chair, Advisor\u003C\/p\u003E\u003Cp\u003EDr. Asif Khan, ECE, Co-Advisor\u003C\/p\u003E\u003Cp\u003EDr. Callie Hao, ECE\u003C\/p\u003E\u003Cp\u003EDr. Sung-Kyu Lim, ECE\u003C\/p\u003E\u003Cp\u003EDr. Muhannad Bakir, ECE\u003C\/p\u003E\u003Cp\u003EDr. Ada Gavrilovska, Coc\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EModern AI models spend much of their energy moving data between memory and processors. Compute-in-memory removes most of this movement by computing directly inside the memory arrays that store the model. The most efficient analog designs promise up to one hundred times better energy efficiency but are inherently noisy, and the noise erodes model accuracy. This dissertation develops open-source simulation tools that evaluate efficiency and accuracy together and uses them to compare memory technologies, compute circuits, packaging options, and AI model architectures.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Compute-in-Memory for Efficient AI: Cross-Layer Modeling and Hardware-Software Co-Design "}],"uid":"28475","created_gmt":"2026-07-09 19:25:15","changed_gmt":"2026-07-09 19:26:52","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-07-20T13:00:00-04:00","event_time_end":"2026-07-20T15:00:00-04:00","event_time_end_last":"2026-07-20T15:00:00-04:00","gmt_time_start":"2026-07-20 17:00:00","gmt_time_end":"2026-07-20 19:00:00","gmt_time_end_last":"2026-07-20 19:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Room 118, Kendeda ","extras":[],"related_links":[{"url":"https:\/\/teams.microsoft.com\/meet\/27220593758674?p=irKh2QThgVNtpzrX6u","title":"Microsoft Teams Link "}],"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":""}}}