ETDs

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.

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Enabling Deep Learning at Edge: From Efficient and Dynamic Inference to On-Device Learning

Description
In recent years, Artificial Intelligence (AI) (e.g., Deep Neural Networks (DNNs), Transformer) has shown great success in real-world applications due to its superior performance in various cognitive tasks. The impressive performance achieved by AI models normally accompanies the cost of enormous model size and high computational complexity, which significantly hampers…

In recent years, Artificial Intelligence (AI) (e.g., Deep Neural Networks (DNNs), Transformer) has shown great success in real-world applications due to its superior performance in various cognitive tasks. The impressive performance achieved by AI models normally accompanies the cost of enormous model size and high computational complexity, which significantly hampers their implementation on resource-limited Cyber-Physical Systems (CPS), Internet-of-Things (IoT), or Edge systems due to their tightly constrained energy, computing, size, and memory budget. Thus, the urgent demand for enhancing the \textbf{Efficiency} of DNN has drawn significant research interests across various communities. Motivated by the aforementioned concerns, this doctoral research has been mainly focusing on Enabling Deep Learning at Edge: From Efficient and Dynamic Inference to On-Device Learning. Specifically, from the inference perspective, this dissertation begins by investigating a hardware-friendly model compression method that effectively reduces the size of AI model while simultaneously achieving improved speed on edge devices. Additionally, due to the fact that diverse resource constraints of different edge devices, this dissertation further explores dynamic inference, which allows for real-time tuning of inference model size, computation, and latency to accommodate the limitations of each edge device. Regarding efficient on-device learning, this dissertation starts by analyzing memory usage during transfer learning training. Based on this analysis, a novel framework called "Reprogramming Network'' (Rep-Net) is introduced that offers a fresh perspective on the on-device transfer learning problem. The Rep-Net enables on-device transferlearning by directly learning to reprogram the intermediate features of a pre-trained model. Lastly, this dissertation studies an efficient continual learning algorithm that facilitates learning multiple tasks without the risk of forgetting previously acquired knowledge. In practice, through the exploration of task correlation, an interesting phenomenon is observed that the intermediate features are highly correlated between tasks with the self-supervised pre-trained model. Building upon this observation, a novel approach called progressive task-correlated layer freezing is proposed to gradually freeze a subset of layers with the highest correlation ratios for each task leading to training efficiency.
ContributorsYang, Li (Author) / Fan, Deliang (Thesis advisor) / Seo, Jae-Sun (Committee member) / Zhang, Junshan (Committee member) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2023
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Security and Usability in Mobile and IoT Systems

Description
Mobile and Internet-of-Things (IoT) systems have been widely used in many aspects

of human’s life. These systems are storing and operating on more and more sensitive

data of users. Attackers may want to obtain the data to peek at users’ privacy or

pollute the data to cause system malfunction. In addition, these systems…

Mobile and Internet-of-Things (IoT) systems have been widely used in many aspects

of human’s life. These systems are storing and operating on more and more sensitive

data of users. Attackers may want to obtain the data to peek at users’ privacy or

pollute the data to cause system malfunction. In addition, these systems are not

user-friendly for some people such as children, senior citizens, and visually impaired

users. Therefore, it is of cardinal significance to improve both security and usability

of mobile and IoT systems. This report consists of four parts: one automatic locking

system for mobile devices, one systematic study of security issues in crowdsourced

indoor positioning systems, one usable indoor navigation system, and practical attacks

on home alarm IoT systems.

Chapter 1 overviews the challenges and existing solutions in these areas. Chapater

2 introduces a novel system ilock which can automatically and immediately lock the

mobile devices to prevent data theft. Chapter 3 proposes attacks and countermeasures

for crowdsourced indoor positioning systems. Chapter 4 presents a context-aware indoor

navigation system which is more user-friendly for visual impaired people. Chapter

5 investigates some novel attacks on commercial home alarm systems. Chapter 6

concludes the report and discuss the future work.
ContributorsLi, Tao (Author) / Zhang, Yanchao (Thesis advisor) / Xue, Guoliang (Committee member) / Zhang, Junshan (Committee member) / Fan, Deliang (Committee member) / Arizona State University (Publisher)
Created2020