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...
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...
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...
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...