Configurable Computing & Embedded Systems
Team is full
Returning students may still apply
GOALS: The objective of the current class project, Tech Cities, is to research and develop a smart city infrastructure for the Atlanta area with a focus on configurable hardware as a computational platform. Configurable (or reconfigurable) computing combines high-performance hardware with the flexibility of software. This project will explore the use of configurable microelectronics to assist in areas such as high-performance computing, security and trust, and component interfacing. We will also research embedded vision algorithms as well as real-time data analytics and display to provide a framework for better understanding urban environments and human interaction.
METHODS & TECHNOLOGIES: Embedded computing systems; programmable logic devices, e.g. FPGAs; microcontrollers; data analytics; data visualization; web-based dashboards; electronic test and measurement equipment; databases.
RESEARCH/DESIGN ISSUES: Configurable computing uses and tradeoffs; hardware/software co-processing/co-simulation; high-performance computing; digital design and verification EDA tools; trustworthy computing; root-of-trust hardware; secure computing; autonomic computing; real-time data analytics; data display and audification; city-planning.
MEETING TIME: Thurs, 3:00-3:50
ADVISORS: Lee W. Lerner (GTRI)/(ECE), Mike Ruiz (GTRI)
PARTNERS & SPONSORS: Xilinx Inc., AT&T Inc
MAJORS, PREPARATION AND INTERESTS:
- EE, CmpE – Embedded systems, digital design, computer architecture, FPGAs, configurable computing, computer security, circuits
- CS, CM – Big or real-time data analytics, graphs, visualization, information security, databases, human-computer interface
- Other – Environmental researchers, city planners, architects, etc
CONTACT: Lee W. Lerner
- Georgia Tech Configurable Computing Lab
- Example project page: Atlanta BeltLine Social Dashboard
- GTRI Cyber Technology and Information Security Laboratory (CTISL)
- GTRI YouTube Channel
Jay Danner, Linda Wills, Elbert M. Ruiz, and Lee W. Lerner, “Rapid Precedent-Aware Pedestrian and Car Classification on Constrained IoT Platforms,” Proc 14th ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia, Pittsburgh, PA, Oct. 2016.