{"693632":{"#nid":"693632","#data":{"type":"event","title":"Ph.D. Proposal Oral Exam - Wei-Hsing Huang","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003E\u003Cem\u003ECross-Layer Co-Design of Memory-Centric Systems for Efficient and Accurate AI Inference\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Yu, Advisor\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr. Lin, Chair\u003C\/p\u003E\u003Cp\u003EDr. Mukhopadhyay\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe objective of the proposed research is to investigate how numerical representation, computation, and data delivery can be designed together for efficient and accurate memory-centric AI inference. Completed research develops an algorithm\u2013hardware co-design approach for Kolmogorov\u2013Arnold networks, combining hardware-aware quantization, input generation, weight mapping, and model configuration. Circuit and system evaluations demonstrate improvements in hardware efficiency and inference accuracy across lightweight and large-scale applications. Building on this work, the proposed research explores two directions: reducing the effects of numerical deviations in analog memory-based computation, and coordinating precision, data reuse, and memory delivery during inference. These efforts aim to improve energy efficiency and latency while maintaining output quality.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Cross-Layer Co-Design of Memory-Centric Systems for Efficient and Accurate AI Inference"}],"uid":"28475","created_gmt":"2026-10-29 19:17:21","changed_gmt":"2026-10-29 19:19:30","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-02T15:30:00-05:00","event_time_end":"2026-11-02T17:30:00-05:00","event_time_end_last":"2026-11-02T17:30:00-05:00","gmt_time_start":"2026-11-02 20:30:00","gmt_time_end":"2026-11-02 22:30:00","gmt_time_end_last":"2026-11-02 22:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Online","extras":[],"related_links":[{"url":"https:\/\/teams.microsoft.com\/meet\/285877888737920?p=4dJPetGhNYb6Kmwfti","title":"Microsoft Teams Link "}],"groups":[{"id":"434371","name":"ECE Ph.D. Proposal Oral Exams"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"},{"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":""}}}