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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Contributor
Date Range
2010 2019


The Resistive Random Access Memory (ReRAM) is an emerging non-volatile memory technology because of its attractive attributes, including excellent scalability (< 10 nm), low programming voltage (< 3 V), fast switching speed (< 10 ns), high OFF/ON ratio (> 10), good endurance (up to 1012 cycles) and great compatibility with silicon CMOS technology [1]. However, ReRAM suffers from larger write latency, energy and reliability issue compared to Dynamic Random Access Memory (DRAM). To improve the energy-efficiency, latency efficiency and reliability of ReRAM storage systems, a low cost cross-layer approach that spans device, circuit, architecture and system levels is proposed. For …

Contributors
Mao, Manqing, Chakrabariti, Chaitali, Yu, Shimeng, et al.
Created Date
2019

Over the past few decades, the silicon complementary-metal-oxide-semiconductor (CMOS) technology has been greatly scaled down to achieve higher performance, density and lower power consumption. As the device dimension is approaching its fundamental physical limit, there is an increasing demand for exploration of emerging devices with distinct operating principles from conventional CMOS. In recent years, many efforts have been devoted in the research of next-generation emerging non-volatile memory (eNVM) technologies, such as resistive random access memory (RRAM) and phase change memory (PCM), to replace conventional digital memories (e.g. SRAM) for implementation of synapses in large-scale neuromorphic computing systems. Essentially being compact …

Contributors
Chen, Pai-Yu, Yu, Shimeng, Cao, Yu, et al.
Created Date
2018

Static CMOS logic has remained the dominant design style of digital systems for more than four decades due to its robustness and near zero standby current. Static CMOS logic circuits consist of a network of combinational logic cells and clocked sequential elements, such as latches and flip-flops that are used for sequencing computations over time. The majority of the digital design techniques to reduce power, area, and leakage over the past four decades have focused almost entirely on optimizing the combinational logic. This work explores alternate architectures for the flip-flops for improving the overall circuit performance, power and area. It …

Contributors
Yang, Jinghua, Vrudhula, Sarma, Barnaby, Hugh, et al.
Created Date
2018

Deep learning (DL) has proved itself be one of the most important developements till date with far reaching impacts in numerous fields like robotics, computer vision, surveillance, speech processing, machine translation, finance, etc. They are now widely used for countless applications because of their ability to generalize real world data, robustness to noise in previously unseen data and high inference accuracy. With the ability to learn useful features from raw sensor data, deep learning algorithms have out-performed tradinal AI algorithms and pushed the boundaries of what can be achieved with AI. In this work, we demonstrate the power of deep …

Contributors
Mohanty, Abinash, Cao, Yu, Seo, Jae-sun, et al.
Created Date
2018

Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications. In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, …

Contributors
LIU, RUI, Yu, Shimeng, Yu, Shimeng, et al.
Created Date
2018

Articial Neural Network(ANN) has become a for-bearer in the field of Articial Intel- ligence. The innovations in ANN has led to ground breaking technological advances like self-driving vehicles,medical diagnosis,speech Processing,personal assistants and many more. These were inspired by evolution and working of our brains. Similar to how our brain evolved using a combination of epigenetics and live stimulus,ANN require training to learn patterns.The training usually requires a lot of computation and memory accesses. To realize these systems in real embedded hardware many Energy/Power/Performance issues needs to be solved. The purpose of this research is to focus on methods to study …

Contributors
Chowdary, Hidayatullah, Cao, Yu, Seo, JaeSun, et al.
Created Date
2018

The rapid improvement in computation capability has made deep convolutional neural networks (CNNs) a great success in recent years on many computer vision tasks with significantly improved accuracy. During the inference phase, many applications demand low latency processing of one image with strict power consumption requirement, which reduces the efficiency of GPU and other general-purpose platform, bringing opportunities for specific acceleration hardware, e.g. FPGA, by customizing the digital circuit specific for the deep learning algorithm inference. However, deploying CNNs on portable and embedded systems is still challenging due to large data volume, intensive computation, varying algorithm structures, and frequent memory …

Contributors
Ma, Yufei, Vrudhula, Sarma, Seo, Jae-sun, et al.
Created Date
2018

Machine learning technology has made a lot of incredible achievements in recent years. It has rivalled or exceeded human performance in many intellectual tasks including image recognition, face detection and the Go game. Many machine learning algorithms require huge amount of computation such as in multiplication of large matrices. As silicon technology has scaled to sub-14nm regime, simply scaling down the device cannot provide enough speed-up any more. New device technologies and system architectures are needed to improve the computing capacity. Designing specific hardware for machine learning is highly in demand. Efforts need to be made on a joint design …

Contributors
Xu, Zihan, Cao, Yu, Chakrabarti, Chaitali, et al.
Created Date
2017

Vision is the ability to see and interpret any visual stimulus. It is one of the most fundamental and complex tasks the brain performs. Its complexity can be understood from the fact that close to 50% of the human brain is dedicated to vision. The brain receives an overwhelming amount of sensory information from the retina – estimated at up to 100 Mbps per optic nerve. Parallel processing of the entire visual field in real time is likely impossible for even the most sophisticated brains due to the high computational complexity of the task [1]. Yet, organisms can efficiently process …

Contributors
Gorthy, Sai Rama Srivatsava, Cao, Yu, Seo, Jae-sun, et al.
Created Date
2017

The aging mechanism in devices is prone to uncertainties due to dynamic stress conditions. In AMS circuits these can lead to momentary fluctuations in circuit voltage that may be missed by a compact model and hence cause unpredictable failure. Firstly, multiple aging effects in the devices may have underlying correlations. The generation of new traps during TDDB may significantly accelerate BTI, since these traps are close to the dielectric-Si interface in scaled technology. Secondly, the prevalent reliability analysis lacks a direct validation of the lifetime of devices and circuits. The aging mechanism of BTI causes gradual degradation of the device …

Contributors
Patra, Devyani, Cao, Yu, Barnaby, Hugh, et al.
Created Date
2017