{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/101838"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/101838","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Powering Next-Generation Artificial Intelligence by Designing Three-dimensional High-Performance Neuromorphic Computing System with Memristors","abstract":"Human brains can complete numerous intelligent tasks, such as pattern recognition, reasoning, control and movement, with remarkable energy efficiency (20 W). In contrast, a typical computer only recognizes 1,000 different objects but consumes about 250 W power [1]. This performance significant differences stem from the intrinsic different structures of human brains and digital computers. The latest discoveries in neuroscience indicate the capabilities of human brains are attributed to three unique features: (1) neural network structure; (2) spike-based signal representation; (3) synaptic plasticity and associative memory learning [1, 2]. In this dissertation, the next-generation platform of artificial intelligence is explored by utilizing memristors to design a three-dimensional high-performance neuromorphic computing system. The low-variation memristors (fabricated by Virginia Tech) reduce the learning accuracy of the system significantly through adding heat dissipation layers. Moreover, three emerging neuromorphic architectures are proposed showing a path to realizing the next-generation platform of artificial intelligence with self-learning capability and high energy efficiency. At last, an Associative Memory Learning System is exhibited to reproduce an associative memory learning that remembers and correlates two concurrent events (pronunciation and shape of digits) together.","abstract_html":"Human brains can complete numerous intelligent tasks, such as pattern recognition, reasoning, control and movement, with remarkable energy efficiency (20 W). In contrast, a typical computer only recognizes 1,000 different objects but consumes about 250 W power [1]. This performance significant differences stem from the intrinsic different structures of human brains and digital computers. The latest discoveries in neuroscience indicate the capabilities of human brains are attributed to three unique features: (1) neural network structure; (2) spike-based signal representation; (3) synaptic plasticity and associative memory learning [1, 2]. In this dissertation, the next-generation platform of artificial intelligence is explored by utilizing memristors to design a three-dimensional high-performance neuromorphic computing system. The low-variation memristors (fabricated by Virginia Tech) reduce the learning accuracy of the system significantly through adding heat dissipation layers. Moreover, three emerging neuromorphic architectures are proposed showing a path to realizing the next-generation platform of artificial intelligence with self-learning capability and high energy efficiency. At last, an Associative Memory Learning System is exhibited to reproduce an associative memory learning that remembers and correlates two concurrent events (pronunciation and shape of digits) together.","abstract_has_math":false,"creators":["An, Hongyu"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Yi, Yang"],"committee_members":["Ha, Dong S.","Kurdila, Andrew J.","Liu, Lingjia","Zhou, Zhen","Orlowski, Mariusz Kriysztof"],"year":2020,"date_issued":"2020-09-17","date_published":"2020-09-17","updated_at":"2026-07-22T22:20:12Z","subjects":["Memristors","Neuromorphic Computing","Artificial Intelligence"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:27163"],"render_values":[{"text":"vt_gsexam:27163","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/101838","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Yi, Yang"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Ha, Dong S.","Kurdila, Andrew J.","Liu, Lingjia","Zhou, Zhen","Orlowski, Mariusz Kriysztof"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["An, Hongyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-01-12T07:00:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-01-12T07:00:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-09-17"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Memristors","Neuromorphic Computing","Artificial Intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:27163"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/101838"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Human brains can complete numerous intelligent tasks, such as pattern recognition, reasoning, control and movement, with remarkable energy efficiency (20 W). In contrast, a typical computer only recognizes 1,000 different objects but consumes about 250 W power [1]. This performance significant differences stem from the intrinsic different structures of human brains and digital computers. The latest discoveries in neuroscience indicate the capabilities of human brains are attributed to three unique features: (1) neural network structure; (2) spike-based signal representation; (3) synaptic plasticity and associative memory learning [1, 2]. In this dissertation, the next-generation platform of artificial intelligence is explored by utilizing memristors to design a three-dimensional high-performance neuromorphic computing system. The low-variation memristors (fabricated by Virginia Tech) reduce the learning accuracy of the system significantly through adding heat dissipation layers. Moreover, three emerging neuromorphic architectures are proposed showing a path to realizing the next-generation platform of artificial intelligence with self-learning capability and high energy efficiency. At last, an Associative Memory Learning System is exhibited to reproduce an associative memory learning that remembers and correlates two concurrent events (pronunciation and shape of digits) together."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["In this dissertation, the next-generation platform of artificial intelligence is explored by utilizing memristors to design a three-dimensional high-performance neuromorphic computing system. The low-variation memristors (fabricated by Virginia Tech) reduce the learning accuracy of the system significantly through adding heat dissipation layers. Moreover, three emerging neuromorphic architectures are proposed showing a path to realizing the next-generation platform of artificial intelligence with self-learning capability and high energy efficiency. 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In contrast, a typical computer only recognizes 1,000 different objects but consumes about 250 W power [1]. This performance significant differences stem from the intrinsic different structures of human brains and digital computers. The latest discoveries in neuroscience indicate the capabilities of human brains are attributed to three unique features: (1) neural network structure; (2) spike-based signal representation; (3) synaptic plasticity and associative memory learning [1, 2]. In this dissertation, the next-generation platform of artificial intelligence is explored by utilizing memristors to design a three-dimensional high-performance neuromorphic computing system. The low-variation memristors (fabricated by Virginia Tech) reduce the learning accuracy of the system significantly through adding heat dissipation layers. Moreover, three emerging neuromorphic architectures are proposed showing a path to realizing the next-generation platform of artificial intelligence with self-learning capability and high energy efficiency. At last, an Associative Memory Learning System is exhibited to reproduce an associative memory learning that remembers and correlates two concurrent events (pronunciation and shape of digits) together."],"dc:description.abstractgeneral":["In this dissertation, the next-generation platform of artificial intelligence is explored by utilizing memristors to design a three-dimensional high-performance neuromorphic computing system. The low-variation memristors (fabricated by Virginia Tech) reduce the learning accuracy of the system significantly through adding heat dissipation layers. Moreover, three emerging neuromorphic architectures are proposed showing a path to realizing the next-generation platform of artificial intelligence with self-learning capability and high energy efficiency. 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