Virginia Tech
Powering Next-Generation Artificial Intelligence by Designing Three-dimensional High-Performance Neuromorphic Computing System with Memristors
Abstract
dc:description.abstractHuman 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.
Degree
thesis:*- Name thesis:degree_name
- Doctor of Philosophy
- Level thesis:degree_level
- doctoral
- Discipline thesis:degree_discipline
- Electrical Engineering
- Department dc:contributor.department
- Electrical Engineering
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2020
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- An, Hongyu
- Chair dc:contributor.committeechair
-
- Yi, Yang
- Committee members dc:contributor.committeemember
-
- Ha, Dong S.
- Kurdila, Andrew J.
- Liu, Lingjia
- Zhou, Zhen
- Orlowski, Mariusz Kriysztof
Subjects
dc:subject × 3Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
Identifiers
dc:identifier.*- Dc Identifier Other
- vt_gsexam:27163
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/101838