{"622556":{"#nid":"622556","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Insik Yoon","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003EPost-CMOS Memory Technologies and their Applications in Emerging Computing Methods\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. Arijit Raychowdhury, ECE, Chair , Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Asif Khan, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Shimeng Yu, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Titash Rakshit, Samsung\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Suman Datta, Univ of Notre Dame\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe objective of this proposed research is to take a holistic approach to the post-CMOS in\/near-memory processing system design for machine learning and optimizations. We first address the current issues of Spin-Transfer Torque Magnetic Random Access Memory(STT-MRAM) and multi-bit ferroelectric FET in the device level. At the circuit level, the research shows how these issues shape the peripheral circuit of STT-MRAM and ferroelectric FET memory arrays. Lastly, at the system level, the research leads to the efficient memory architecture and system design that maximizes the benefits of STT-MRAM and ferroelectric FET while mitigating the current limitations of these devices. In the proposed research, we apply in\/near memory processing system design with STT-MRAM and ferroelectric FETs to various applications such as reinforcement learning with a drone, image classification with Deep Neural Network and least square minimization for image reconstruction. For the remaining part of this research, we will focus on near-memory processing system with STT-MRAM for reinforcement learning of a drone and evaluate the system to quantify how much benefits are expected in terms of latency, power and energy. From this project, we would like to show that near-memory processing system with non-volatile devices is a key enabler for real-time learning systems with stringent power and energy constraints.\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Post-CMOS Memory Technologies and their Applications in Emerging Computing Methods "}],"uid":"28475","created_gmt":"2019-06-17 20:46:01","changed_gmt":"2019-06-25 17:59:20","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2019-06-28T17:30:00-04:00","event_time_end":"2019-06-28T19:30:00-04:00","event_time_end_last":"2019-06-28T19:30:00-04:00","gmt_time_start":"2019-06-28 21:30:00","gmt_time_end":"2019-06-28 23:30:00","gmt_time_end_last":"2019-06-28 23:30:00","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":""}}}