Description
This research mission looks at electrochemical synapses as building blocks to emulate and advance learning models. What can we learn from biological synapses to build better, more energy-efficient engineered hardware? Science research goals include modeling the circuits that both underlie complex learned behaviors and begin to emulate and advance state of the art learning rules.
Engineering goals include building bio-inspired energy efficient hardware, new computing architectures, neural networks, and integrated high-density circuits
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Breene M. Kerr (1951) Professor, Department of Nuclear Science and EngineeringProfessor, Department of Materials Science and Engineering- Nanotechnology
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Donner Professor, Department of Electrical Engineering and Computer ScienceMacVicar Faculty Fellow- AI Hardware
- Efficient AI
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Professor of the Practice, Department of Electrical Engineering and Computer Science- AI Hardware
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Associate Professor, Department of Electrical Engineering and Computer ScienceResearch Laboratory of Electronics- AI Hardware
- Efficient AI
- Computer Vision
- Machine Learning
- AI Robotics
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Glen V. and Phyllis F. Dorflinger Professor, Brain and Cognitive SciencesDepartment Head, Brain and Cognitive SciencesInvestigator, McGovern Institute- Computational Neuroscience
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Battelle Energy Alliance Professor, Deparment of Nuclear Science and EngineeringProfessor, Department of Materials Science and Engineering- AI Materials
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Advanced Television and Signal Processing Professor of Electrical EngineeringDirector of Center for Integrated Circuits and SystemsMissions