Integrated Computational Electronics Laboratory (ICE)

Is that layout on those tiles?

Welcome to the ICE Laboratory Site @ GT

The lab has two current focus directions:

  • Programmable and configurable, analog and digital, circuits, signal processing, algorithms, and systems
  • Neuromorphic Engineering
From these two areas of focus, we research signal processing, analog and digital integrated circuits and systems design, computational neuroscience, nonlinear dynamics, and CMOS device physics.

Lead Professor: Dr. Jennifer Hasler

Topically Organized Publications

Overview writings

Recent Courses Taught

Recent article on the importance of the Faculty--Ph.D. Student Mentoring Relationship (GT press: April 11, 2016)

Programmability and Configurability

Neuromorphic Computation

We want to have systems based on neuroscience, to perform biological like tasks, including robotics, and thereby contribute to neuroscience understanding.

References :
[1] S. George, S. Kim, S. Shah, et. al, "A Programmable and Configurable Mixed-Mode FPAA SOC,” IEEE Transactions on VLSI, 2016.
[2] M. Collins, J. Hasler, and S. George, "An Open-Source Toolset Enabling Analog–Digital–Software Codesign," Journal of Low Power Electronics Applications, January 2016.
[3] J. Hasler, S. Kim, S. Shah, F. Adil, M. Collins, S. Koziol, and S. Nease, "Transforming Mixed-Signal Circuits Class through SoC FPAA IC, PCB, and Toolset," European Workshop on Microelectronics Education, Southampton, May 2016.
[4] J. Hasler, S. Shah, S. Kim, I. Lal, and M. Collins, "Remote FPAA System Setup Enabling Wide Accessibility of Configurable devices," Journal of Low Power Electronics Applications, June 2016.
[5] S. Kim, J. Hasler, and S. George, "Integrated Floating-Gate Programming Environment for System-Level Ics," IEEE Transactions on VLSI , 2016.
[6] Electronic Product Magazine, March 21, 2016. "New analog chip uses 1,000 times less electrical power (and can be built a hundred times smaller) than comparable digital devices"
References :
[1] J. Hasler, H. B. Marr, "Finding a Roadmap to achieve Large Neuromorphic Hardware Systems," Frontiers in Neuroscience, vol. 7, no. 118, September 2013. pp. 1-29. DOI=10.3389/fnins.2013.00118 [2]