The results of our research on silicon implementation of classification algorithms in neural devices has been presented among four invited talks at the NAE Frontiers of Engineering Symposium. The symposium covers cutting-edge developments in four areas: Mega-Tall Buildings and Other Future Places of Work, Unraveling the Complexity of the Brain, Energy Strategies to Power Our Future, and Machines That Teach Themselves. The mission of the NAE is to advance the well-being of the nation by promoting a vibrant engineering profession and by marshalling the expertise and insights of eminent engineers to provide independent advice to the federal government on matters involving engineering and technology.
The related paper is selected for publication in both the National Academy of Engineering’s annual report, the Bridge, and the FOE report:
M. Shoaran, B. A. Haghi, M. Farivar, A. Emami, “Efficient Feature Extraction and Classi fication Methods in Neural Interfaces,” National Academy of Engineering’s annual report, the Bridge: Unraveling the Complexity of the Brain, 2017
