Organizer: | |
Time: | 9:00 am - 10:00 am (PT), Thu, Feb 11, 2021 |
Abstract: | Through this panel discussion, we wish to provide a novel perspective regarding neuromorphic hardware: as accelerators for solving large-scale optimization problems, with orders of magnitude gains in performance and energy. Following the idea of architecture-algorithm co-design, we will discuss how the architectural features of neuromorphic hardware enable efficient solutions of certain optimization problems as well as how the algorithmic features of certain optimization problems make them amenable to efficient neuromorphic solutions. We will build on state-of-the-art research of the panelists (e.g., mapping genetic algorithms, NP-hard problems like travelling salesman, and minimax dynamics onto spiking networks), in addition to the results obtained at Intel Labs on constraint satisfaction, graph search, and sparse coding. |
Panelists: |
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Pre-requisites/ co-requisites: | Attending Neuromorphic Solutions for Optimization Problems I will be useful, but not required |
Recording: | Not available yet |
Link to Presentation: | Not available yet |
Previous INRC Forum Presentations by the Panelists: |
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