Von-Neumann and Beyond: Memristor Architectures

Handle URI:
http://hdl.handle.net/10754/623652
Title:
Von-Neumann and Beyond: Memristor Architectures
Authors:
Naous, Rawan ( 0000-0001-6129-7926 )
Abstract:
An extensive reliance on technology, an abundance of data, and increasing processing requirements have imposed severe challenges on computing and data processing. Moreover, the roadmap for scaling electronic components faces physical and reliability limits that hinder the utilization of the transistors in conventional systems and promotes the need for faster, energy-efficient, and compact nano-devices. This work thus capitalizes on emerging non-volatile memory technologies, particularly the memristor for steering novel design directives. Moreover, aside from the conventional deterministic operation, a temporal variability is encountered in the devices functioning. This inherent stochasticity is addressed as an enabler for endorsing the stochastic electronics field of study. We tackle this approach of design by proposing and verifying a statistical approach to modelling the stochastic memristors behaviour. This mode of operation allows for innovative computing designs within the approximate computing and beyond Von-Neumann domains. In the context of approximate computing, sacrificing functional accuracy for the sake of energy savings is proposed based on inherently stochastic electronic components. We introduce mathematical formulation and probabilistic analysis for Boolean logic operators and correspondingly incorporate them into arithmetic blocks. Gate- and system-level accuracy of operation is presented to convey configurability and the different effects that the unreliability of the underlying memristive components has on the intermediary and overall output. An image compression application is presented to reflect the efficiency attained along with the impact on the output caused by the relative precision quantification. In contrast, in neuromorphic structures the memristors variability is mapped onto abstract models of the noisy and unreliable brain components. In one approach, we propose using the stochastic memristor as an inherent source of variability in the neuron that allows it to produce spikes stochastically. Alternatively, the stochastic memristors are mapped onto bi-stable stochastic synapses. The intrinsic variation is modelled as added noise that aids in performing the underlying computational tasks. Both aspects are tested within a probabilistic neural network operation for a handwritten MNIST digit recognition application. Synaptic adaptation and neuronal selectivity are achieved with both approaches, which demonstrates the savings, interchangeability, robustness, and relaxed design space of brain-inspired unconventional computing systems.
Advisors:
Salama, Khaled N. ( 0000-0001-7742-1282 )
Committee Member:
Magistretti, Pierre J. ( 0000-0002-6678-320X ) ; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 ) ; Shihada, Basem ( 0000-0003-4434-4334 ) ; Kurdahi, Fadi
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Electrical Engineering
Issue Date:
May-2017
Type:
Dissertation
Appears in Collections:
Dissertations

Full metadata record

DC FieldValue Language
dc.contributor.advisorSalama, Khaled N.en
dc.contributor.authorNaous, Rawanen
dc.date.accessioned2017-05-17T09:10:52Z-
dc.date.available2017-05-17T09:10:52Z-
dc.date.issued2017-05-
dc.identifier.urihttp://hdl.handle.net/10754/623652-
dc.description.abstractAn extensive reliance on technology, an abundance of data, and increasing processing requirements have imposed severe challenges on computing and data processing. Moreover, the roadmap for scaling electronic components faces physical and reliability limits that hinder the utilization of the transistors in conventional systems and promotes the need for faster, energy-efficient, and compact nano-devices. This work thus capitalizes on emerging non-volatile memory technologies, particularly the memristor for steering novel design directives. Moreover, aside from the conventional deterministic operation, a temporal variability is encountered in the devices functioning. This inherent stochasticity is addressed as an enabler for endorsing the stochastic electronics field of study. We tackle this approach of design by proposing and verifying a statistical approach to modelling the stochastic memristors behaviour. This mode of operation allows for innovative computing designs within the approximate computing and beyond Von-Neumann domains. In the context of approximate computing, sacrificing functional accuracy for the sake of energy savings is proposed based on inherently stochastic electronic components. We introduce mathematical formulation and probabilistic analysis for Boolean logic operators and correspondingly incorporate them into arithmetic blocks. Gate- and system-level accuracy of operation is presented to convey configurability and the different effects that the unreliability of the underlying memristive components has on the intermediary and overall output. An image compression application is presented to reflect the efficiency attained along with the impact on the output caused by the relative precision quantification. In contrast, in neuromorphic structures the memristors variability is mapped onto abstract models of the noisy and unreliable brain components. In one approach, we propose using the stochastic memristor as an inherent source of variability in the neuron that allows it to produce spikes stochastically. Alternatively, the stochastic memristors are mapped onto bi-stable stochastic synapses. The intrinsic variation is modelled as added noise that aids in performing the underlying computational tasks. Both aspects are tested within a probabilistic neural network operation for a handwritten MNIST digit recognition application. Synaptic adaptation and neuronal selectivity are achieved with both approaches, which demonstrates the savings, interchangeability, robustness, and relaxed design space of brain-inspired unconventional computing systems.en
dc.language.isoenen
dc.subjectApproximate computingen
dc.subjectMemristoren
dc.subjectNeuromorphicen
dc.subjectStochastic electronicsen
dc.titleVon-Neumann and Beyond: Memristor Architecturesen
dc.typeDissertationen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberMagistretti, Pierre J.en
dc.contributor.committeememberAlouini, Mohamed-Slimen
dc.contributor.committeememberShihada, Basemen
dc.contributor.committeememberKurdahi, Fadien
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.nameDoctor of Philosophyen
dc.person.id129080en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.