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

Potential Senior Design / Graduate Special Problems directions

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

Some background on my former graduate students

Some of Dr. Hasler's personal thoughts (under construction)

"if you build it, you understand it. And if you understand it, you can build it" -- Carver Mead

Programmability and Configurability

Physical and Neuromorphic Computation

Neuromorphic Systems: 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] 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, "We could build an artificial brain right now," IEEE Spectrum , June 2017. [2] 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 [3] J. Hasler, "Starting Framework for Analog Numerical Analysis for Energy Efficient Computing", Journal of Low Power Electronics Applications, vol. 7, no. 17, June 2017. pp. 1-22. [4] J. Hasler, “Opportunities in Physical Computing driven by Analog Realization,” IEEE IC Rebooting Computing , San Deigo, October 2016.
[5] H. B. Marr and J. Hasler, "Compiling probabilistic, bio-inspired circuits on a field programmable analog array," Frontiers in Neuroscience, 2015. pp. 1-9.