Parallel Hardware Applications in Science and Technology
Team Advisor/PI
Project Description/Research Team Goals
Recent advances in VLSI technology are enabling fast computing systems with tens and hundreds of processing units. These range from ASICs to field programmable gate arrays (FPGA) to graphics processing units (GPU) to multi-core processors, such as the Intel Xeon. These parallel systems can be used to accelerate applications in wireless communications, image processing, data science, and medical devices. Current projects focus on machine learning, wireless energy transfer, communications, and control for cardiac devices with the Texas Heart Institute.
Issues Addressed
- Machine Learning
- Parallel and embedded computing
- Medical devices
Research Methods and Technology
- Various computer aided design and simulation tools for modeling of electrical and mechanical systems
Preferred Undergraduate Interests
- Machine learning
- Embedded computing hardware and software
- Medical devices
Academic Majors of Interest
Limited to: ECE; MECH; COMP
Prior Preparation/Requisite Experience
- Matlab
- C/C++
- Python
Compensation
Work study-eligible students may receive compensation from OURI.
Course Credit
ELEC 491(undergraduate); ELEC 591 (graduate)
Team Meeting
Group meeting once a week; Additional meetings with graduate student mentors
Actively Onboarding New Members
Yes
Contact
For more information, please email Prof. Cavallaro (cavallar@rice.edu) and/or visit the team's VIP page.