Toward Brain-inspired, Energy-efficient Chips
Traditional computer chips waste time and energy shuttling data between separate memory and computational units. Neural circuits in the brain, by contrast, achieve enormous efficiencies by storing and processing information at the same place. Inspired by biological models of learning, researchers are designing computing elements that mimic neural circuits and consume massively less energy.
Speakers
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Breene M. Kerr (1951) Professor, Department of Nuclear Science and EngineeringProfessor, Department of Materials Science and Engineering- Nanotechnology
Missions -
Glen V. and Phyllis F. Dorflinger Professor, Brain and Cognitive SciencesDepartment Head, Brain and Cognitive SciencesInvestigator, McGovern Institute- Computational Neuroscience
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Donner Professor, Department of Electrical Engineering and Computer ScienceMacVicar Faculty Fellow- AI Hardware
- Efficient AI
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Battelle Energy Alliance Professor, Deparment of Nuclear Science and EngineeringProfessor, Department of Materials Science and Engineering- AI Materials
Missions
Schedule
Schedule
Date: Friday, March 26, 2021
Time: 12pm - 1pm EST
Where: Zoom Webinar
Introduction
Aude Oliva
Electrochemical Analog Synapses for Energy-efficient Brain-inspired Computing
Bilge Yildiz
Vocal Learning in the Songbird:
A Model for Complex Learned Behaviors
Michale Fee
Roundtable Discussion
Bilge Yildiz, Michale Fee, Jesus del Alamo, Ju Li
Q&A
Wrap up
Aude Oliva
Register
Register HereSeminar Organizers
Aude Oliva
Emily Goldman
Kim Martineau
Allison Provaire
Samantha Smiley
