University of Wisconsin Madison College of Engineering

Computing and information processing

Developing computational technologies and information processing techniques that allow us to store, communicate and manipulate data—as well as extract knowledge from that data—is a major electrical and computer engineering research focus
at UW-Madison. 

Throughout the past decades, revolutions in computing technologies have made fast and massively capable devices widely available. Formulating the enabling insights required to continue that trajectory — leading to smaller, lower power, more specialized devices —
is our focus. Electrical and computer engineering researchers at UW-Madison explore topics that include embedded systems, computer architecture, circuit design, and computational architectures
and paradigms inspired by biology.

In parallel with this technology revolution has been an explosion in the type and variety of data that is available. Such data need to be turned into useful and actionable knowledge, as well as transported to the appropriate end users. Electrical and computer engineering researchers in this area draw upon a variety of disciplines in applied mathematics, statistics, machine learning, and the cognitive sciences to tackle problems in signal and image processing, information and communication theory, cryptography and security, and a variety
of applications in the medical and health sciences. 



Reconfigurable hardware could boost computer performance


A University of Wisconsin-Madison professor is studying how to use reconfigurable hardware to implement a wide range of computer accelerators that boost performance and increase energy efficiency.


Katherine Compton

Katherine Compton

The reconfigurable hardware is flexible enough to allow developers
to customize the accelerators to execute multiple applications, and the research has earned Electrical and Computer Engineering Assistant Professor Katherine Compton a prestigious National Science Foundation CAREER award.


Compton says the idea of her work is similar to cooking. Chefs can make anything from a cookbook, but they can make a dish much faster if they memorize the recipe. A traditional central processing unit, or CPU, is like a chef with a cookbook; it can process anything, but it’s relatively slow since it processes data sequentially and has to look up the instructions every time, even if it's handled the same task before.


Application-specific integrated circuits, called ASICs, are hard-coded at the factory with a single “memorized” recipe. This hardware is fast because it doesn't need to look up the instructions, and
it processes data in parallel, meaning it handles multiple data threads simultaneously like a graphics processing unit, or GPU.


Reconfigurable hardware is a flexible form of special-purpose hardware that is like a super chef who can quickly memorize or re-memorize a small set of recipes. Like ASICs, the hardware memorizes functions and performs computations in parallel. Reconfigurable hardware goes beyond ASICs by loading sets of data that determine which wires should be connected or disconnected, thereby creating different digital circuits for different tasks.


For example, the hardware could load an MP3 encoder accelerator to compress an audio file and then quickly switch to become a decryption accelerator.


Compton's research focuses on how a computing system determines which accelerators should be loaded into hardware at any given time. Since first publishing on system-level reconfigurable hardware management in 2005, Compton has studied how to allocate the accelerators in response to, but in isolation of, the rest of the computer system. The CAREER award, which comes with a five-year grant of more than $407,000, will allow her to expand her work to study the entire system and schedule multiple computing resources to work in tandem with the reconfigurable hardware.


In terms of the cooking metaphor, she essentially is looking at the entire kitchen workflow to determine how the various chefs — in other words, the CPU, GPU and reconfigurable hardware can best work together to most efficiently make the dish, or execute an application.


Ultimately, Compton is working to demonstrate to hardware companies that reconfigurable hardware provides enough of a boost to warrant adding it to everyday computing devices. “We're looking at having potentially the same or faster processing speed, but with lower energy consumption,” she says.


Turning down the noise helps researchers ‘listen’ to the brain


Professor Barry Van Veen (center) with students (from left) Patrick Cheung, Pam Limpiti, Andrew Bolstad and Matt Rebholtz.

Professor Barry Van Veen (center) with students (from left) Patrick Cheung, Pam Limpiti, Andrew Bolstad and Matt Rebholtz

To study how regions of the brain communicate, neuroscientists often use a technique called electroencephalography (EEG), which reads electrical activity in the brain through sensors on the scalp.


However, the skull and the scalp blur these EEG readings. In addition, a multitude of signals from “background” processes make it difficult to pinpoint electrical activity corresponding to specific tasks. “It’s like standing outside a crowded party and trying to sort out individual conversations,” says Professor
Barry Van Veen.


Van Veen and his students use signal-processing techniques to filter out that noise and enable them to study how one area of the brain influences another. “The brain is active all the time,” he says. “It’s in the midst of that background noise that you have to identify a specific set of connections associated with a task.”


One research paradigm is working memory, a type of task-oriented short-term memory. For example, working memory allows a person to remember a phone number long enough to dial it, or to remember a series of notes or pattern of shapes long enough to repeat it. Neuroscientists hypothesize that several regions of the brain are connected in working memory tasks. Van Veen and his students use their signal-processing techniques to identify electrical connections from EEG data and determine how they change under different conditions, such as task difficulty or recall accuracy.

Illustration of EEG brain study.


The group also is interested in how connectivity in the brain changes between waking and sleep, and more complicated activity such as language processing.


Van Veen is hopeful that, as the research progresses, his methods will provide some insight into the workings of the brain and lead to better understanding for treatment of medical conditions like epilepsy or schizophrenia.