Fault Tolerant Computing  Dr. Kieckhafer is heading an area focused on fault tolerant distributed computing. The core of this work involves voting algorithms that allow redundant computers to come to agreement by eliminating the most unlikely fault mode. This work has a broad range of relevance. |
Embedded systems and Artificial Intelligence Derived Biosensor Devices  This is application-driven research focused on the use of artificial intelligence and embedded systems in biosensors and imaging devices. One of the technologies that is being developed involves a device that traps and kills HIV infected cells. The device conceivably would be implanted into the lymph system and proactively recruit infected cells. Additionally, research is focused on a sensor to map the progression of brain cancer using 3-D mathematical modeling and an embedded systems approach. While these are early stage technologies, the architecture that enables functionality of the sensors involves the generation of a circuit without using a microprocessor. The research has yielded a way to use JAVA to create the circuit. This concept could have the potential to be used in a number of different applications, including wide use in the biosensor field. |
Soft Computing and Embedded Systems This program of research is focused on two areas. The first involves the development of soft computing techniques and their applications to computer learning and pattern recognition. Specific research topics include classification and regression trees, fuzzy systems, global optimization algorithms, and fuzzy-neural computing. The second involves the development of an interactive, subroutine-threaded programming language for embedded systems. It also includes the study of issues related to the design of embedded systems including hardware/software co-design, microcontrollers, and FPGA synthesis using VHDL. |
Text, Image and Video Databases This research program focuses on a topics associated with motion analysis and object tracking, document image processing, pattern recognition and machine learning. More generally, issues under investigation include data mining in text, image and video databases as well as neural networks design and application. |
Numerical Speedup Using Flowpaths Applications for computer simulations include many research areas such as weather prediction, tracking the location and concentrations of contaminants in groundwater, oil recovery, studying disease processes, designing experiments, and developing medications. In these and several other applications, it is desirable to achieve speedup of numerical code. Current work in speeding up numerical simulations has several disadvantages. Considering the various disadvantages of each method, project will develop methods that increases the speed and (1) does not require rewriting an existing algorithm, although could be improved even further by making minor coding modification, (2) does not require algorithms written in traditional languages to be rewritten in other language, (3) executes portions of the code in parallel but does not suffer from the overhead of either a single microprocessor or multi-processor architecture, and (4)does not require time and effort to engineer and implement a special circuit for different types of numerical algorithms. This work proposes to develop such a technology using flowpaths where, starting with a C (or potentially FORTRAN) description of a numerical algorithm, a compiler will generate an executable that can be downloaded and will run on the Power PC embedded in an FPGA with parallel flowpaths to speedup the bottleneck loops in the numerical algorithm automatically.
With such a speed-up, some simulations that require real-time execution that can not currently be achieved by a PC will be able to run at a higher speed and achieve a real-time pace. The success of this research will result in future investigation including deriving optimizations for the compiler and resulting circuits, improving numerical schemes for optimal implementation in hardware and enhancing the compiler to support other popular languages.
The intellectual merit of this research project from a scientific computational standpoint lies in the discovery of new coding techniques that make optimal use of flowpaths in order to achieve higher simulation speeds. The intellectual merit in hardware design for speedup lies in the unique use of flowpaths for creating special-purpose processors for new and existing numerical code, automatically. This project serves as a novel interdisciplinary approach, combining expertise in scientific computation of numerical algorithms and high-speed embedded systems for significantly increasing the performance of numerical code, with impact both in software as well as in hardware technologies. |
home | about us | contact us
Powered by IEI © 2006 - 2010 | All rights reserved.