Browsing by Subject "Computer engineering"
Now showing items 1-20 of 96
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A Data-Intensive Framework for Analyzing Dynamic Supreme Court Behavior
(2012)Many law professors and scholars think of the Supreme Court as a black box--issues and arguments go in to the Court, and decisions come out. The almost mystical nature that these researchers impute to the Court seems to ... -
A Formal Framework for Designing Verifiable Protocols
(2017)Protocols play critical roles in computer systems today, including managing resources, facilitating communication, and coordinating actions of components. It is highly desirable to formally verify protocols, to provide a ... -
Accelerated Motion Planning Through Hardware/Software Co-Design
(2019)Robotics has the potential to dramatically change society over the next decade. Technology has matured such that modern robots can execute complex motions with sub-millimeter precision. Advances in sensing technology have ... -
Accelerating Data Parallel Applications via Hardware and Software Techniques
(2020)The unprecedented amount of data available today opens the door to many new applications in areas such as finance, scientific simulation, machine learning, etc. Many such applications perform the same computations on different ... -
Accelerating Probabilistic Computing with a Stochastic Processing Unit
(2020)Statistical machine learning becomes a more important workload for computing systems than ever before. Probabilistic computing is a popular approach in statistical machine learning, which solves problems by iteratively generating ... -
Accelerator Architectures for Deep Learning and Graph Processing
(2020)Deep learning and graph processing are two big-data applications and they are widely applied in many domains. The training of deep learning is essential for inference and has not yet been fully studied. With data forward, ... -
Adaptive Methods for Machine Learning-Based Testing of Integrated Circuits and Boards
(2020)The relentless growth in information technology and artificial intelligence (AI) is placing demands on integrated circuits and boards for high performance, added functionality, and low power consumption. As a result, design ... -
Advancing the Design and Utility of Adversarial Machine Learning Methods
(2021)While significant progress has been made to craft Deep Neural Networks (DNNs) with super-human recognition performance, their reliability and robustness in challenging operating conditions is still a major concern. In this ... -
Algorithm-hardware co-optimization for neural network efficiency improvement
(2020)Deep neural networks (DNNs) are tremendously applied in the artificial intelligence field. While the performance of DNNs is continuously improved by more complicated and deeper structures, the feasibility of deployment on ... -
Anomaly-Detection and Health-Analysis Techniques for Core Router Systems
(2018)A three-layer hierarchy is typically used in modern telecommunication systems in order to achieve high performance and reliability. The three layers, namely core, distribution, and access, perform different roles for service ... -
Applying Machine Learning to Testing and Diagnosis of Integrated Systems
(2021)The growing complexity of integrated boards and systems makes manufacturing test and diagnosis increasingly expensive. There is a pressing need to reduce test cost and to pinpoint the root causes of integrated systems in ... -
Architectures for Memristor-based Storage Structures
(2011)Rapid data growth nowadays makes it more critical to reduce search time to improve the performance of search-intensive applications. However, huge data size makes it more difficult to efficiently perform search operations. ... -
Autonomous Sensor Path Planning and Control for Active Information Gathering
(2014)Sensor path planning and control refer to the problems of determining the trajectory and feedback control law that best support sensing objectives, such as monitoring, detection, classification, and tracking. Many autonomous ... -
Bayesian Nonparametric Modeling of Latent Structures
(2014)Unprecedented amount of data has been collected in diverse fields such as social network, infectious disease and political science in this information explosive era. The high dimensional, complex and heterogeneous data imposes ... -
Characterizing and Mitigating Errors in Quantum Computers
(2023)This thesis aims to present methods for characterizing and mitigating errors in quantum computers. We begin by providing a historical overview of computing devices and the evolution of quantum information. The basics of ... -
Closed-Loop Deep Brain Stimulation in Parkinson’s Disease with Distributed, Proportional plus Integral Control
(2022)Continuous deep brain stimulation (cDBS) of either subthalamic nucleus (STN) or globus pallidus (GP) is an effective therapy in Parkinson’s Disease (PD) but is inherently limited by lack of responsiveness to dynamic, fluctuating ... -
Coordinating Software and Hardware for Performance Under Power Constraints
(2019)For more than 50 years since its birth in 1965, Moore's Law has been a self-fulfilling prophecy that drives computing forward. However, as Dennard scaling ends, chip power density presents a challenge that becomes increasingly ... -
Coordinating the Design and Management of Heterogeneous Datacenter Resources
(2014)Heterogeneous design presents an opportunity to improve energy efficiency but raises a challenge in management. Whereas prior work separates the two, we coordinate heterogeneous design and management. We present a market-based ... -
Coset Coding to Extend the Lifetime of Non-Volatile Memory
(2014)Modern computing systems are increasingly integrating both Phase Change Memory (PCM) and Flash memory technologies into computer systems being developed today, yet the lifetime of these technologies is limited by the number ... -
Cumulon: Simplified Matrix-Based Data Analytics in the Cloud
(2016)Cumulon is a system aimed at simplifying the development and deployment of statistical analysis of big data in public clouds. Cumulon allows users to program in their familiar language of matrices and linear algebra, without ...
