The performance of computers has improved tremendously in the past four decades, which has enabled innumerable applications that have major roles in our daily lives. However, without dramatic innovations in improving power efficiency of computing, the continued semiconductor device scaling alone will fail to provide sufficient performance for the future computing capabilities. For the emerging challenge, the proposed research investigates revolutionary computing paradigms to process, comprehend, and use abundant data in an extremely efficient way. The novelty of the approach lies in developing power- and performance-efficient computing systems with software support by exploiting the characteristics of future workloads that often use complex probabilistic mathematical models of physical phenomena. In such applications, approximate computing can often result in satisfactory outcomes. Meanwhile, it can dramatically decrease power consumption or increase performance by replacing complete logic functions with simplified circuits that mimic the functions for rough calculations. To extend the approximate computing concept to more general-purpose computing systems, the following holistic approaches are proposed: 1) intelligent microarchitectures to identify correctness-non-critical regions of code with compiler support; 2) approximate computing engines to execute such regions of code power and performance efficiently; 3) high-level morphic primitives to process a large fraction of workloads with orders-of-magnitude greater power and performance efficiency; and 4) flexible architectures to allow programmers and users to trade the quality of computing with the efficiency.

The proposed research will have a specific and significant impact on the computer architecture, circuit, and compiler communities since it requires analysis of interesting and representative workloads; realization of state-of-the-art circuit, architecture, and compiler infrastructure; and invention of powerful and useful evaluation methodologies. Since most of the development and research work will be conducted by graduate students, both industry and academia will benefit from well-educated and trained employees as well as direct technology transfer when students graduate and begin employment elsewhere. Finally, the success of this research will tremendously benefit our ability to advance human?s collective knowledge in science, technology, business, medicine, and virtually every other field of human endeavor by allowing remarkable improvement in computing performance.

Agency
National Science Foundation (NSF)
Institute
Division of Computer and Communication Foundations (CCF)
Application #
0953603
Program Officer
Hong Jiang
Project Start
Project End
Budget Start
2010-03-15
Budget End
2015-02-28
Support Year
Fiscal Year
2009
Total Cost
$345,733
Indirect Cost
Name
University of Wisconsin Madison
Department
Type
DUNS #
City
Madison
State
WI
Country
United States
Zip Code
53715