{"681578":{"#nid":"681578","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Xiangyu Mao","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; Efficient Hardware Architectures for High-Throughput Streaming Data RF and AI-Driven Communication Systems\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Saibal Mukhopadhyay, ECE, Chair, Advisor\u003C\/p\u003E\u003Cp\u003EDr. Justin Romberg, ECE\u003C\/p\u003E\u003Cp\u003EDr. Suman Datta, ECE\u003C\/p\u003E\u003Cp\u003EDr. Callie Hao, ECE\u003C\/p\u003E\u003Cp\u003EDr. Hyesoon Kim, CoC\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EWith the rapid evolution of fifth-generation (5G) and the emergence of sixth-generation (6G) wireless communication systems, the need for large-scale, real-time data processing has become increasingly critical across a wide range of applications. This growth is paralleled by the exponential rise in AI model complexity and data generation, driving demand for hardware architectures that can efficiently handle high-throughput, low-latency computation. Despite these advances, hardware development for data-intensive systems has significantly lagged behind, creating a bottleneck for practical deployment. This dissertation addresses this gap by proposing novel hardware solutions in three key areas: (1) a system-level RF radar emulator based on the Direct Path Computing Model; (2) the first digital ultra-low-bit precision linear embedded beamforming system with quantization compensation; and (3) a Processing-in-Memory (PIM) computing architecture optimized for AI-enhanced automotive radar applications. Each contribution is designed to overcome existing limitations in data movement, scalability, and power efficiency, offering a cohesive framework for next-generation RF and AI communication systems. Collectively, this work advances the development of high-performance, energy-efficient, and scalable hardware platforms capable of meeting the growing computational demands of the 6G era and beyond.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Efficient Hardware Architectures for High-Throughput Streaming Data RF and AI-Driven Communication Systems "}],"uid":"28475","created_gmt":"2025-04-03 19:41:45","changed_gmt":"2025-04-03 19:42:44","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-18T15:30:00-04:00","event_time_end":"2025-04-18T17:30:00-04:00","event_time_end_last":"2025-04-18T17:30:00-04:00","gmt_time_start":"2025-04-18 19:30:00","gmt_time_end":"2025-04-18 21:30:00","gmt_time_end_last":"2025-04-18 21:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Room 2108, Klaus","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":""}}}