{"645221":{"#nid":"645221","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Xiaochen Peng","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003EBenchmark framework for 2-D\/3-D integrated compute-in-memory based machine learning accelerator\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. Sung-Kyu Lim, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Saibal Mukhopadhyay, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Muhannad Bakir, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Michael Niemier, U of Notre Dame\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003ENeural-inspired compute-in-memory (CIM) accelerators with emerging non-volatile memory (eNVM) devices such as resistive random access memory (RRAM) have been proven in silicon for deep learning acceleration. We proposed an end-to-end benchmark framework for the software and hardware evaluation of CIM accelerators with versatile device technologies called DNN+NeuroSim. The proposed framework can support both inference and training chip evaluation, with wide range of technology parameters, from 130nm down to 7nm. Furthermore, as the 3-D integration was proposed as a promising solution to support high bandwidth and on-chip storage for machine learning platforms, we proposed the 3D+NeuroSim with extend 3-D featured parameters and integrated thermal model, to evaluate the monolithic and heterogeneous 3-D integrated CIM accelerators. We have done comprehensive benchmarks across different 2-D and 3-D integrated CIM accelerators and versatile device technologies to explore various design options, and released the proposed frameworks as public tools for research community.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Benchmark framework for 2-D\/3-D integrated compute-in-memory based machine learning accelerator "}],"uid":"28475","created_gmt":"2021-03-11 00:22:03","changed_gmt":"2021-03-11 00:22:03","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2021-03-15T14:00:00-04:00","event_time_end":"2021-03-15T16:00:00-04:00","event_time_end_last":"2021-03-15T16:00:00-04:00","gmt_time_start":"2021-03-15 18:00:00","gmt_time_end":"2021-03-15 20:00:00","gmt_time_end_last":"2021-03-15 20:00:00","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":""}}}