{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115631"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115631","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Acceleration of deep learning applications using Intel distribution of OpenVINO toolkit","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Li, Haoxiang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Kindratenko, Volodymyr"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["OpenVINO","Machine Learning","Convolution Neural Network","Power-efficiency"],"languages":["en","eng"],"rights":["Copyright 2022 Haoxiang Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115631","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kindratenko, Volodymyr"]},{"key":"dc:creator","label":"Author","values":["Li, Haoxiang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-29"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["OpenVINO","Machine Learning","Convolution Neural Network","Power-efficiency"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Haoxiang Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115631"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Haoxiang Li, accepted the attached license on 2022-04-29 at 11:12.","The student, Haoxiang Li, submitted this Thesis for approval on 2022-04-29 at 11:14.","This Thesis was approved for publication on 2022-04-29 at 11:47.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18009 on 2022-11-11 at 12:12:22","Machine learning has been a popular domain of research for the past decade. The emergence of deep neural networks (DNN) brings new solutions to address complex problems, including image classification, object detection, and natural language processing (NLP). The use of convolution and deep architecture allows information to be extracted and learned effectively from large-scale datasets and has led to significant technological breakthroughs in many traditional scientific fields. In particular, astrophysicists have proposed deep learning solutions for tasks related to gravitational waves, such as detecting and characterizing such events. To satisfy the increasing demand from researchers, many open-source frameworks, such as TensorFlow and PyTorch have been developed for ease of use. With the assistance of large-scale distributed GPU systems, researchers are able to develop, train, and test domain-specific deep learning applications efficiently. However, many of such applications require deployment on edge devices, collecting and processing data in real-time. In this case, GPU may not provide a portable solution to DNN inference because they are expensive and power-inefficient. In contrast, other hardware architectures such as CPU, VPU, and FPGA can be more accessible to a larger range of customers and more applicable to many power-restricted scenarios. In this thesis, we are interested in accelerating DNN inference workload using Intel Distribution of OpenVINO toolkit on various Intel hardware. We explore the process of model conversion, workload deployment in Intel DevCloud, and performance benchmarks for several popular networks. In addition, we evaluate the inference performance of a specific deep learning algorithm developed for multi-messenger astrophysics to characterize complex gravitational waves caused by the merging of binary black holes. We make a comparative analysis in terms of inference throughput and power consumption on PyTorch with Nvidia GPUs and OpenVINO with Intel CPUs, GPUs, and VPUs."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Acceleration of deep learning applications using Intel distribution of OpenVINO toolkit"]}]}],"canonical_facts":{"dc:contributor":["Kindratenko, Volodymyr"],"dc:creator":["Li, Haoxiang"],"dc:date":["2022-05","2022-04-29"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Haoxiang Li, accepted the attached license on 2022-04-29 at 11:12.","The student, Haoxiang Li, submitted this Thesis for approval on 2022-04-29 at 11:14.","This Thesis was approved for publication on 2022-04-29 at 11:47.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18009 on 2022-11-11 at 12:12:22","Machine learning has been a popular domain of research for the past decade. The emergence of deep neural networks (DNN) brings new solutions to address complex problems, including image classification, object detection, and natural language processing (NLP). The use of convolution and deep architecture allows information to be extracted and learned effectively from large-scale datasets and has led to significant technological breakthroughs in many traditional scientific fields. In particular, astrophysicists have proposed deep learning solutions for tasks related to gravitational waves, such as detecting and characterizing such events. To satisfy the increasing demand from researchers, many open-source frameworks, such as TensorFlow and PyTorch have been developed for ease of use. With the assistance of large-scale distributed GPU systems, researchers are able to develop, train, and test domain-specific deep learning applications efficiently. However, many of such applications require deployment on edge devices, collecting and processing data in real-time. In this case, GPU may not provide a portable solution to DNN inference because they are expensive and power-inefficient. In contrast, other hardware architectures such as CPU, VPU, and FPGA can be more accessible to a larger range of customers and more applicable to many power-restricted scenarios. In this thesis, we are interested in accelerating DNN inference workload using Intel Distribution of OpenVINO toolkit on various Intel hardware. We explore the process of model conversion, workload deployment in Intel DevCloud, and performance benchmarks for several popular networks. In addition, we evaluate the inference performance of a specific deep learning algorithm developed for multi-messenger astrophysics to characterize complex gravitational waves caused by the merging of binary black holes. We make a comparative analysis in terms of inference throughput and power consumption on PyTorch with Nvidia GPUs and OpenVINO with Intel CPUs, GPUs, and VPUs."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115631"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Haoxiang Li"],"dc:subject":["OpenVINO","Machine Learning","Convolution Neural Network","Power-efficiency"],"dc:title":["Acceleration of deep learning applications using Intel distribution of OpenVINO toolkit"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}