Reducing the size and power of stochastic computing neural networks through training.

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dc.contributor.advisorKoziol, Scott M.
dc.creatorCarrano, Matthew J., 1996-
dc.date.accessioned2021-10-08T13:50:12Z
dc.date.available2021-10-08T13:50:12Z
dc.date.created2021-08
dc.date.issued2021-08-01
dc.date.submittedAugust 2021
dc.date.updated2021-10-08T13:50:13Z
dc.description.abstractIt has been demonstrated that stochastic computing (SC) has the ability to reduce the size and power requirements of artificial neural network (ANN) circuits [1]. There are two prevailing SC neuron topologies: multiplexer (MUX) and approximate parallel counter (APC) based [2]. Both topologies contain an activation module with a state parameter that affects the respective output function as well as the size and power requirements. This thesis explores altering this state parameter and the network training process in order to reduce the size and power of each neuron without incurring significant accuracy loss. As part of this exploration, a stochastic artificial neural network (SANN) is created in Verilog and implemented on a Field Programmable Gate Array (FPGA). Additionally, a SANN simulator is built in MATLAB to assist in rapid prototyping. Both simulation and hardware results demonstrate that the size/power utilized by SANNs can be reduced without significant accuracy loss.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2104/11641
dc.language.isoen
dc.rights.accessrightsNo access – contact librarywebmaster@baylor.edu
dc.subjectStochastic computing. Machine learning. Field programmable gate array (FPGA). Multilayer perceptron (MLP). Artificial neural network (ANN).
dc.titleReducing the size and power of stochastic computing neural networks through training.
dc.typeThesis
dc.type.materialtext
local.embargo.lift2026-08-01
local.embargo.terms2026-08-01
thesis.degree.departmentBaylor University. Dept. of Electrical & Computer Engineering.
thesis.degree.grantorBaylor University
thesis.degree.levelMasters
thesis.degree.nameM.S.E.C.E.

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