Title:
Energy-efficient digital hardware platform for learning complex systems

dc.contributor.author Kung, Jae Ha
dc.contributor.committeeMember Mukhopadhyay, Saibal
dc.contributor.committeeMember Yalamanchili, Sudhakar
dc.contributor.committeeMember Raychowdhury, Arijit
dc.contributor.committeeMember Kim, Hyesoon
dc.contributor.committeeMember Chai, Sek
dc.contributor.department Electrical and Computer Engineering
dc.date.accessioned 2017-06-07T17:48:57Z
dc.date.available 2017-06-07T17:48:57Z
dc.date.created 2017-05
dc.date.issued 2017-04-10
dc.date.submitted May 2017
dc.date.updated 2017-06-07T17:48:57Z
dc.description.abstract System learning is the most fundamental research area in engineering domain. It is a modeling method to map external inputs to the corresponding outputs with/without physically analyzing the system between them. The system can be simple enough, e.g. a linear time-invariant system, to be easily identified by a simple mathematical model. However, it can be a more complex system, such as a nonlinear dynamic system, which is highly difficult to understand with mathematical representations. In this thesis, energy-efficient digital hardware to understand a wide range of complex systems using different approaches is presented. For a model-based approach, a programmable and efficient hardware for simulating dynamical systems is presented. The proposed platform accelerates the computation of solving a wide class of differential equations by utilizing a computing model called cellular nonlinear network with novel system architecture. As a data-driven approach, several neural network algorithms are selected for the system learning. The focused system is related to vision tasks such as image or video processing. Several design algorithms and analysis to realize low-power neural network accelerators are presented. The proposed low-power design methods are not limited to certain tasks, but are based on algorithmic analysis for general applicability.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/58316
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Energy-efficiency
dc.subject Digital accelerator
dc.title Energy-efficient digital hardware platform for learning complex systems
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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