Post-CMOS memory technologies and their applications in emerging computing models

dc.contributor.advisor Raychowdhury, Arijit
dc.contributor.author Yoon, Insik
dc.contributor.committeeMember Khan, Asif Islam
dc.contributor.committeeMember Yu, Shimeng
dc.contributor.committeeMember Rakshit, Titash
dc.contributor.committeeMember Datta, Suman
dc.contributor.department Electrical and Computer Engineering
dc.date.accessioned 2019-08-21T13:54:30Z
dc.date.available 2019-08-21T13:54:30Z
dc.date.created 2019-08
dc.date.issued 2019-07-10
dc.date.submitted August 2019
dc.date.updated 2019-08-21T13:54:30Z
dc.description.abstract The objective of this proposed research is to take a holistic approach to the post-CMOS in/near-memory processing system design for machine learning and optimizations. We first address the current issues of Spin-Transfer Torque Magnetic Random Access Memory(STT-MRAM) and multi-bit ferroelectric FET in the device level. At the circuit level, the research shows how these issues shape the peripheral circuit of STT-MRAM and ferroelectric FET memory arrays. Lastly, at the system level, the research leads to the efficient memory architecture and system design that maximizes the benefits of STT-MRAM and ferroelectric FET while mitigating the current limitations of these devices. In the proposed research, we apply the in/near memory processing system design with STT-MRAM and ferroelectric FETs to various applications such as reinforcement learning with a drone, image classification with Deep Neural Network and least square minimization for image reconstruction. For the remaining part of this research, we will focus on near-memory processing system with STT-MRAM for reinforcement learning of a drone and evaluate the system to quantify how much benefits are expected in terms of latency, power and energy.From this project, we would like to show that near-memory processing system with nonvolatile devices is a key enabler for real-time learning systems with stringent power and energy constraints.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/61768
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Post-CMOS memory
dc.subject STT-MRAM
dc.subject Ferroelectric FET
dc.subject Reinforcement learning
dc.subject In-memory computing
dc.subject Convex optimization
dc.title Post-CMOS memory technologies and their applications in emerging computing models
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Raychowdhury, Arijit
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication c44dbd39-c229-4ffb-9bc0-007eb0904114
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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