{"651861":{"#nid":"651861","#data":{"type":"event","title":"Ph.D. Proposal Oral Exam - Sheng-Chun Kao","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003E\u003Cem\u003EHardware and Dataflow Optimization for Efficient DNN Accelerators\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u0026nbsp; \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Krishna, Advisor\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Hao, Chair\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Sarkar\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003EThe objective of this research is to leverage ML techniques to optimize the designflow of the DNN accelerator. Many SOTA DNN accelerator designs still rely on a human-driven design process which costs huge engineering effort for each new generation of DNNaccelerators and becomes increasingly harder to keep up with the innovation speed of DNNmodels.\u0026nbsp; There is two main focus of accelerator design:\u0026nbsp; HW resource configuration andmapping where mapping can further be categorized to intra-layer mapping and inter-layermapping.\u0026nbsp;\u0026nbsp; Finally\u0026nbsp; in the new trend of multi-accelerator platform\u0026nbsp; the mapping acrossaccelerator becomes the fourth dimension of the accelerator design. This research target topropose an ML-based solution for each of the four major steps in DNN accelerator design.In this research we present (1) an ML-assisted HW resource allocation method drivenby the RL-based algorithm (2) an automatic intra-layer mapping search tool driven by aGA-based algorithm and finally we plan to develop (3) an ML-based technique to optimizeinter-layer mapping and (4) an ML-based solution to optimize cross-accelerator mapping.These ML-assisted design tools can be used independently to swap out part of the manual-tuning process in the lengthy and engineer-intensive DNN accelerator design process orused simultaneously to reveal a full-fledged ML-assisted DNN accelerator design flow.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Hardware and Dataflow Optimization for Efficient DNN AcceleratorsHardware and Dataflow Optimization for Efficient DNN Accelerators"}],"uid":"28475","created_gmt":"2021-10-20 17:43:13","changed_gmt":"2021-10-20 17:43:13","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2021-11-10T15:00:00-05:00","event_time_end":"2021-11-10T17:00:00-05:00","event_time_end_last":"2021-11-10T17:00:00-05:00","gmt_time_start":"2021-11-10 20:00:00","gmt_time_end":"2021-11-10 22:00:00","gmt_time_end_last":"2021-11-10 22:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"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":""}}}