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Stochastic-Based Computing with Emerging Spin-Based Device Technologies
- Date Issued:
- 2016
- Abstract/Description:
- In this dissertation, analog and emerging device physics is explored to provide a technology plat- form 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 sus- ceptibility 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 compu- tational 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 hys- teresis 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 imple- mentation, when compared with its deterministic-based ANN counterparts implemented with dig- ital 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.
Title: | Stochastic-Based Computing with Emerging Spin-Based Device Technologies. |
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Name(s): |
Bai, Yu, Author Lin, Mingjie, Committee Chair DeMara, Ronald, Committee CoChair Wang, Jun, Committee Member Jin, Yier, Committee Member Dong, Yajie, Committee Member University of Central Florida, Degree Grantor |
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Type of Resource: | text | |
Date Issued: | 2016 | |
Publisher: | University of Central Florida | |
Language(s): | English | |
Abstract/Description: | In this dissertation, analog and emerging device physics is explored to provide a technology plat- form 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 sus- ceptibility 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 compu- tational 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 hys- teresis 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 imple- mentation, when compared with its deterministic-based ANN counterparts implemented with dig- ital 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. | |
Identifier: | CFE0006680 (IID), ucf:51921 (fedora) | |
Note(s): |
2016-08-01 Ph.D. Engineering and Computer Science, Electrical Engineering and Computer Engineering Doctoral This record was generated from author submitted information. |
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Subject(s): | Emerging Device -- Stochastic Computing -- Neural Network | |
Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0006680 | |
Restrictions on Access: | public 2017-02-15 | |
Host Institution: | UCF |