{"663289":{"#nid":"663289","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Yandong Luo","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003EEnergy Efficient On-chip Deep Neural Network (DNN) Inference and Training with Emerging Non-volatile Memory Technologies\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. Shimeng Yu, ECE, Chair, Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Callie Hao, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Yingyan Lin, CS\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Tushar Krishna, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Saibal Mukhopadhyay, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003EEmerging non-volatile memory (eNVM) technologies are providing new opportunities for designing DNN accelerators with high energy efficiency. In this thesis, DNN accelerator designs using the eNVM-based compute-in-memory (CIM) paradigm and high-density on-chip buffer are proposed. For DNN inference, a CIM accelerator with a reconfigurable interconnect is presented. It optimizes the communication pattern by using application-specific interconnect topology. To support the multi-head self-attention (MHSA) mechanism in transformers, a heterogeneous computing platform with CIM and a digital sparse engine is utilized for the various types of matrix-matrix multiplications involved. A CIM-based approximate computing scheme is proposed to support the run-time sparsity in attention score computation. For DNN training, to overcome the high write energy of eNVM, a hybrid weight cell design using eNVM and a capacitor is proposed for the weight update during training. To store large volumes of intermediate data during training, a dual-mode buffer design is proposed based on ferroelectric materials. It optimizes both the dynamic read\/write energy and the standby power by operating at volatile and non-volatile modes.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Energy Efficient On-chip Deep Neural Network (DNN) Inference and Training with Emerging Non-volatile Memory Technologies "}],"uid":"28475","created_gmt":"2022-11-17 20:32:59","changed_gmt":"2022-11-17 20:36:45","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2022-11-28T10:30:00-05:00","event_time_end":"2022-11-28T12:30:00-05:00","event_time_end_last":"2022-11-28T12:30:00-05:00","gmt_time_start":"2022-11-28 15:30:00","gmt_time_end":"2022-11-28 17:30:00","gmt_time_end_last":"2022-11-28 17:30:00","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":""}}}