Professor, EECS Graduate Program Director
Electrical Engineering & Computer Science
- AMPS (Advanced Microprocessor and Power-aware Systems)
- Dynamic power and thermal management for computer systems
- Power and performance optimization of energy harvesting real-time embedded systems
- Neuromorphic computing and high performance computing for cognitive applications
Excessive energy dissipation has become one of the limiting factors that prevents the sustained growth of computation power of IT facilities. High power consumption reduces system reliability, increases energy and cooling cost, and cuts the battery cycle time of mobile devices. Aiming at curbing the system energy dissipation, green computing has attracted substantial interests in recent years. Dr. Qiu’s primary research interest covers different areas in green computing, from runtime power and thermal management of computer systems to energy harvesting real-time embedded system. The goal of her research is to provide machine intelligence to today’s computing platforms to achieve autonomous resource management with energy and thermal awareness.
Her second research area is architecture design of neuromorphic computing. Neuromorphic computing refers to the emerging computation concept inspired by the principles of information processing in human neural system. It is widely accepted that human beings are much superior to machines in some areas such as image recognition. With the increase of our knowledge on brain function and our capability in realizing massive parallel computation and communication, it is time to investigate new algorithm and hardware architecture for signal processing and perception. Dr. Qiu’s research focuses on the software and hardware development for such computing systems.
- VLSI Design
- Computer architecture
- ACM SIGDA Distinguished Service Award (2011)
- NSF Career Award (2009)
- American Society for Engineering Education (ASEE) Summer Research Faculty Fellowship (2007)
Shen, Y. Tan, J. Lu, Q. Wu and Qinru Qiu, “Achieving Autonomous Power Management Using Reinforcement Learning,”ACM Transactions on Design Automation of Electronic Systems, Vol. 18, Iss. 2, pp. 24032, March 2013.
Ge, Qinru Qiu, and Q. Wu, “A Multi-Agent Framework for Thermal Aware Task Migration in Many-Core Systems,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Volume: 20 , Issue: 10, pp. 1758 – 1771, 2012.
Liu, J. Liu, Q. Wu and Qinru Qiu, “Harvesting-Aware Power Management for Real-Time Systems with Renewable Energy,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Volume: 20 , Issue: 8, pp. 1473 – 1486, 2012.
Qinru Qiu, Q. Wu, M. Bishop, R. Pino, and R. W. Linderman, “A Parallel Neuromorphic Text Recognition System and Its Implementation on a Heterogeneous High Performance Computing Cluster,” IEEE Transactions on Computers, Digital Object Identifier: 10.1109/TC.2012.50.
H. Lu, Qinru Qiu, A. R. Butt and K. W. Cameron, “End-to-End Energy Management,” Computer, 44 (11), November 2011.