Abstract
In this dissertation, analog and emerging device physics is explored to provide a technology platform to design new bio-inspired system and novel architecture. With CMOS approaching the nano-scaling, their physics limits in feature size. Therefore, their physical device characteristics will pose severe challenges to constructing robust digital circuitry. Unlike transistor defects due to fabrication imperfection, quantum-related switching uncertainties will seriously increase their susceptibility to noise, thus rendering the traditional thinking and logic design techniques inadequate. Therefore, the trend of current research objectives is to create a non-Boolean high-level computational model and map it directly to the unique operational properties of new, power efficient, nanoscale devices. The focus of this research is based on two-fold: 1) Investigation of the physical hysteresis switching behaviors of domain wall device. We analyze phenomenon of domain wall device and identify hysteresis behavior with current range. We proposed the Domain-Wall-Motion-based (DWM) NCL circuit that achieves approximately 30x and 8x improvements in energy efficiency and chip layout area, respectively, over its equivalent CMOS design, while maintaining similar delay performance for a one bit full adder. 2) Investigation of the physical stochastic switching behaviors of Mag- netic Tunnel Junction (MTJ) device. With analyzing of stochastic switching behaviors of MTJ, we proposed an innovative stochastic-based architecture for implementing artificial neural network (S-ANN) with both magnetic tunneling junction (MTJ) and domain wall motion (DWM) devices, which enables efficient computing at an ultra-low voltage. For a well-known pattern recognition task, our mixed-model HSPICE simulation results have shown that a 34-neuron S-ANN implementation, when compared with its deterministic-based ANN counterparts implemented with digital and analog CMOS circuits, achieves more than 1.5 ~ 2 orders of magnitude lower energy consumption and 2 ~ 2.5 orders of magnitude less hidden layer chip area.
Notes
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Graduation Date
2016
Semester
Summer
Advisor
Lin, Mingjie
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Electrical Engineering and Computer Engineering
Degree Program
Electrical Engineering
Format
application/pdf
Identifier
CFE0006680
URL
http://purl.fcla.edu/fcla/etd/CFE0006680
Language
English
Release Date
February 2017
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Bai, Yu, "Stochastic-Based Computing with Emerging Spin-Based Device Technologies" (2016). Electronic Theses and Dissertations. 5424.
https://stars.library.ucf.edu/etd/5424