We address the problem of efficient maintenance of the answer to a new type of query: Continuous Maximizing Range-Sum (Co-MaxRS) for moving objects trajectories. The traditional static/spatial MaxRS problem finds a location for placing the centroid of a given (axes-parallel) rectangle $R$ so that the sum of the weights of...
Power and energy are becoming the limiting factors for computer designs and systems, and energy efficient functional units are getting more popular in such systems. Some of the design methodologies that are getting more common include voltage overscaling and employing imprecise instructions. These functional units need to be characterized correctly...
Abstract The work presented in this dissertation addresses three broad areas of video signal processing: video transmission, motion estimation and error concealment. In the first category, focused on the source-side, we present two machine learning models for efficient content-aware resource allocation and packet prioritization for video transmission over shared/constrained, lossy...
Circuit-level Dynamic Timing Slack (DTS) has emerged as a compelling opportunity for eliminating inefficiency in modern low-power systems. This slack arises when all the signals have propagated through logic paths well in advance of the clock signal. When it is properly identified, the system can exploit this unused cycle time...
One method of cancer treatment is to thermally ablate (destroy) tumor masses using heat caused by electric current, or Joule heating. This particular modality is called radiofrequency ablation (RFA), due to the use of electric currents in the 100 kHz to 800 kHz frequency range. Computationally, RFA is simulated as...
This thesis explains the works that have been completed towards the Ph.D. thesis of the author and discusses the conclusions derived from the results, as well as what future holds for stress-related mobile health research. Main focus of the thesis is use of wearable sensors to understand physiological manifestation of...
Deep learning is a new area of machine learning research that allows deep neural networks composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Deep learning has helped in achieving the objective of pushing machine learning closer to one of its original goals of...
The active participation of external entities in the design and manufacturing flow has produced numerous hardware security issues. Among all the hardware security problems, piracy and overproduction are likely to be the most ubiquitous and expensive ones. Most leading-edge design houses have outsourced their fabrication to the offshore foundries for...
As technology scales down, challenges in fabrication, thermal stress, and in-field degradation have put the reliability of processors at risk. Among different fault types, transient faults manifest themselves frequently due to high chip density, aggressive voltage scaling, and high clock frequency. Some dependable processor architectures have been proposed to counter...
While the entire silicon industry has been blooming under Moore’s Law for decades, conventional digital implementation is approaching the “stall” of Moore’s Law due to many physical design limitations. Technology innovation now is going to take a different direction. Given the increasing demand for emerging applications' computational capacity, it is...
Over the last few years, understanding user experience within mobile systems has become a popular phenomenon as a means to manage hardware resources. Across the many issues being studied in this area, I focus on how to utilize user satisfaction in this dissertation. Notably, I examine user experience by incorporating...
The era of big data creates opportunities for carrying out scientific simulations at exascale.With increasing data size, the complexity of the design and execution of scientific
applications demand the use of high-level tools, namely workflow systems on supercomputers.
The performance of workflow systems has paramount importance since the goal of...
The integration of field-programmable gate arrays (FPGAs) into large scale computing systems is gaining attention. In these systems, real-time data handling for networking, tasks for scientific computing, and machine learning can be executed with customized datapaths on reconfigurable fabric within heterogeneous compute nodes. At the same time, high-level synthesis (HLS)...
Recently, a myriad of applications take advantage of deep learning methods to solve regression/classification problems. Although deep neural networks have shown powerful learning capability, many deep learning applications suffer from the extremely time-consuming training of the neural networks. In order to reduce the training time, researchers usually consider parallel training...
Deep learning is a new area of machine learning in artificial intelligence that consists of networks to learn representations from data in a supervised, semi-supervised and unsupervised manner. Deep learning has a relatively long history, but it does not gain great attention until big data and fast computational resources are...
Cyber-physical systems (CPS), as a multidisciplinary area, have been widely adopted in our daily life and attract experts from various fields. CPS aims to achieve real-time and resilient connection with physical world through integration of computation, communication and control technologies. Many CPS systems, such as automotive, avionics, and industrial system,...
Security and robustness are two critical problems in modern computing system. In this disserta- tion, we study these two problems in both hardware system and learning system.Firstly, we discuss the robustness problem in hardware system. Modern microprocessors suffer from significant on-chip variation at the advanced technology nodes. The development of...
Recent developments in deep learning have led to breakthroughs in rendering novel views from sparse input views of a scene.While the accuracy of these algorithms has improved dramatically, it has come at a huge computational cost.
While developments in graphics hardware have ameliorated some of the computational burdens, deep learning-based...
This research looks at the robotic shape formation problem, which is one of the fundamental problems in robotic swarm systems. Here, the task is to move a group of robots to form a user-specified shape. In this dissertation, the task of shape formation is divided to four problems: (i) using...
With the rapid advancement of machine learning techniques (in particular deep neural networks), computer vision applications have shown great promises in a variety of domains for intelligent cyber-physical systems (CPSs), such as autonomous driving, medical imaging, and vision-based robotic systems. However, while many vision applications provide great on-paper performance, their...
With growing system complexity and closer cyber-physical interaction, there are stronger needs for cyber-physical systems to adapt to the dynamic environment and improve their runtime performance. However, especially for safety-critical systems, the ability of such adaptation and improvement is often restricted by multiple factors, such as limited resources, stringent timing...
Computer systems supported by photonic interconnects and photonic memory devices can reach performance and energy efficiency levels unattainable through purely electronic means across scales, from processor chips to the data center. However, the promised benefits cannot be realized through a simple replacement process; to reach their full potential, several aspects...
Memory management and address translation need significant optimizations in order to not behindrances in the near future. Currently, plenty of work has started to address issues within the
current abstraction of the hardware-software codesign of paging. I argue that a new abstraction
is needed in order to properly address this...
The speed of the storage device has long lagged behind the computation speed of processors.As a result, the I/O performance of storage systems in a supercomputer fails to keep up with its computational power.
This gap continues to widen in modern supercomputers.
On future exascale supercomputers, this issue can worsen...
The speed of the storage device has long lagged behind the computation speed of processors.As a result, the I/O performance of storage systems in a supercomputer fails to keep up with its computational power.
This gap continues to widen in modern supercomputers.
On future exascale supercomputers, this issue can worsen...
Machine learning is seeping into every fabric in various practical domains such as autonomous driving, wearable computing, and smart buildings. However, in the actual development and integration, especially when the learning-based components are frequently included as components of large complex systems where the physical instances can be included as interactable...
Machine learning-based techniques have shown great promises in perception, prediction, planning, and general decision-making for improving task performance of autonomous driving. Connectivity technology has also presented great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction capabilities of individual vehicles. However,...
Mission-critical systems are those imperative systems whose failures can result in catastrophic consequences. Traditional techniques, such as manual investigation and testing, cannot ensure the absence of errors and security vulnerabilities within these systems. This dissertation leverages formal methods to comprehensively examine several mission-critical systems and their essential components. For each...