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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.


Language
  • English
Date Range
2011 2019


The past decade has seen a tremendous surge in running machine learning (ML) functions on mobile devices, from mere novelty applications to now indispensable features for the next generation of devices. While the mobile platform capabilities range widely, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful CPUs with GPUs to accelerate the computation of deep neural networks (DNNs), which are the most common structures to perform ML operations. But traditional von Neumann architectures are not optimized for the high memory bandwidth …

Contributors
Kadetotad, Deepak Vinayak, Seo, Jae-sun, Chakrabarti, Chaitali, et al.
Created Date
2019

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

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

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

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

Speech recognition and keyword detection are becoming increasingly popular applications for mobile systems. While deep neural network (DNN) implementation of these systems have very good performance, they have large memory and compute resource requirements, making their implementation on a mobile device quite challenging. In this thesis, techniques to reduce the memory and computation cost of keyword detection and speech recognition networks (or DNNs) are presented. The first technique is based on representing all weights and biases by a small number of bits and mapping all nodal computations into fixed-point ones with minimal degradation in the accuracy. Experiments conducted on the …

Contributors
Arunachalam, Sairam, Chakrabarti, Chaitali, Seo, Jae-sun, et al.
Created Date
2016

The recent flurry of security breaches have raised serious concerns about the security of data communication and storage. A promising way to enhance the security of the system is through physical root of trust, such as, through use of physical unclonable functions (PUF). PUF leverages the inherent randomness in physical systems to provide device specific authentication and encryption. In this thesis, first the design of a highly reliable resistive random access memory (RRAM) PUF is presented. Compared to existing 1 cell/bit RRAM, here the sum of the read-out currents of multiple RRAM cells are used for generating one response bit. …

Contributors
Shrivastava, Ayush, Chakrabarti, Chaitali, Yu, Shimeng, et al.
Created Date
2015

The aging process due to Bias Temperature Instability (both NBTI and PBTI) and Channel Hot Carrier (CHC) is a key limiting factor of circuit lifetime in CMOS design. Threshold voltage shift due to BTI is a strong function of stress voltage and temperature complicating stress and recovery prediction. This poses a unique challenge for long-term aging prediction for wide range of stress patterns. Traditional approaches usually resort to an average stress waveform to simplify the lifetime prediction. They are efficient, but fail to capture circuit operation, especially under dynamic voltage scaling (DVS) or in analog/mixed signal designs where the stress …

Contributors
Sutaria, Ketul, Cao, Yu, Bakkaloglu, Bertan, et al.
Created Date
2014

Non-volatile memories (NVM) are widely used in modern electronic devices due to their non-volatility, low static power consumption and high storage density. While Flash memories are the dominant NVM technology, resistive memories such as phase change access memory (PRAM) and spin torque transfer random access memory (STT-MRAM) are gaining ground. All these technologies suffer from reliability degradation due to process variations, structural limits and material property shift. To address the reliability concerns of these NVM technologies, multi-level low cost solutions are proposed for each of them. My approach consists of first building a comprehensive error model. Next the error characteristics …

Contributors
Yang, Chengen, Chakrabarti, Chaitali, Cao, Yu, et al.
Created Date
2014

ABSTRACT Developing new non-traditional device models is gaining popularity as the silicon-based electrical device approaches its limitation when it scales down. Membrane systems, also called P systems, are a new class of biological computation model inspired by the way cells process chemical signals. Spiking Neural P systems (SNP systems), a certain kind of membrane systems, is inspired by the way the neurons in brain interact using electrical spikes. Compared to the traditional Boolean logic, SNP systems not only perform similar functions but also provide a more promising solution for reliable computation. Two basic neuron types, Low Pass (LP) neurons and …

Contributors
An, Pei, Cao, Yu, Barnaby, Hugh, et al.
Created Date
2013