specific platform, for instance, could significantly reduce the time a patient must spend in a CT scan machine. Other computational approaches to making scanning and image processing more powerful, such as massive parallel processing by general-purpose computers, are highly inefficient, and cloud computing poses possible data access and privacy issues, Cong says.
Kok-Kiong Yap, left, a Ph.D. candidate in electrical engineering at stanford university, explaining the award-winning poster “openRoads: empowering Research in mobile networks” to an attendee at siGcomm 2009.
than model checking. In their Expeditions project, the researchers will take advantage of the strengths of both methods by tightly integrating the two into what they call MCAI 2.0.
“These domain challenges are at opposite ends of the spectrum of size,” says Clarke, co-winner of the ACM A.M. Turing Award in 2007. (The 2007 Turing Award lecture, “Model Checking: Algorithmic Verification and Debugging,” begins on p. 74.) “One is at the cellular level and the other is at the size of a 747 or Airbus A380—yet we believe that many of the same problems you need for handling complexity at one level will apply to the other as well.”
en task much more easily than someone who is not similarly trained, Cong’s team will explore creating a computing platform with what he calls a “very flexible” processor core with customizable elements such as operating frequency, voltage, and cache sizes, as well as a domain-specific programming fabric and a radio frequency-based communications fabric that can be tuned to multiple applications. All of the customizable computing and communications elements will be managed by a stack of intelligent software.
The UCLA-led project will focus on adapting these platforms for medical imaging and hemodynamic simulation. A successful imaging domain-
Jason Cong, professor of computer science and engineering at UCLA’s Center for Domain-Specific Computing, likens the computing platform his team is devising to a human brain.
“If you look to the evolution of humans in the last 5,000 to 10,000 years, I don’t think the number of neurons in the brain has changed that much, nor has the firing speed of neurons; we’re all wired in a very similar way,” Cong says. “So, I believe a lot of progress is done through specialization.”
Just as training in a specific discipline leads one person to perform a giv-
The RoboBee researchers will explore complementary elements of creating robotic bees using three vectors modeled on live insects—body, brain, and colony. Topics within the body research includes all aspects of flight apparatus, propulsion, and power systems. The brain experiments involve research on the electronic nervous system equivalent of a bee’s brain, including circuits for sensing and decision making. Colony research entails communication and control algorithms that will enable performance beyond the capabilities of an individual. The research team includes experts in biology, computer science, electrical and mechanical engineering, and materials science.
“We are trying to develop micro-mechanical devices, power electronics, and low-power computing fabrics we need to instrument the brain, and to wrap it all together, this notion of developing algorithms and simulating a whole colony of individual agents,” says co-principal investigator Gu-Yeon Wei, associate professor of electrical engineering at Harvard’s School of Engineering and Applied Sciences.
The researchers will also create an exhibit at the Museum of Science in Boston, which will explore the life of bees and the technologies required to create RoboBees. “Bees elicit a lot of excitement and imagery from young to old,” Wei says. “We felt that a good way of tying it together is to share our research in an easily accessible manner.”
Computational Sustainability: Computational Methods for a Sustainable Environment, Economy, and Society cornell university This Expedition applies techniques from computer science and related
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