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dc.contributor.advisorMukhopadhyay, Saibal
dc.contributor.authorLong, Yun
dc.date.accessioned2020-09-08T12:38:45Z
dc.date.available2020-09-08T12:38:45Z
dc.date.created2019-05
dc.date.submittedMay 2019
dc.identifier.urihttp://hdl.handle.net/1853/63494
dc.description.abstractModeling the dynamics of biophysical neural network (BNN) is essential to understand brain operation and design cognitive systems. Large-scale and biophysically plausible BNN modeling requires solving multiple-terms, coupled and non-linear differential equations, making simulation computationally complex and memory intensive. In this work, an adaptive simulation methodology is presented in which neurons in the region of interest (ROI) follow high biological accurate models while the other neurons follow computation friendly models. To enable ROI based approximation, we propose a generic template based computing algorithm which unifies the data structure and computing flow for various neuron models. We implement the algorithms on CPU, GPU and embedded platforms, showing 11x speedup with insignificant loss of biological details in the region of interest.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectSpiking neural network
dc.subjectMachine learning
dc.subjectROI based approximation
dc.subjectGPU computing
dc.titleAccelerating biophysical neural network simulation with region of interest based approximation
dc.typeThesis
dc.description.degreeM.S.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelMasters
dc.contributor.committeeMemberRaychowdhury, Arijit
dc.contributor.committeeMemberKhan, Asif Islam
dc.date.updated2020-09-08T12:38:45Z


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