Neuromorphic computing for temporal scientific data classification

• Autores: Schuman C.D., Potok T.E., Young S., Patton R., Perdue G., Chakma G., Wyer A., Rose G.S.
• Formato: objeto de conferencia
• Fecha de Publicación: 2017
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/626
• Enlace del recurso: https://hdl.handle.net/20.500.12390/626; https://doi.org/10.1145/3183584.3183612
• Nivel de acceso: acceso abierto
• Materia: Spiking neural networks; Neural networks; Convolutional neural network; Neuromorphic; Neuromorphic computing; Scientific data; Classification (of information); https://purl.org/pe-repo/ocde/ford#1.02.00

This material is based upon work supported in part by the U.S. Department of Energy, Ofce of Science, Ofce of Advanced Scientifc Computing Research, under contract number DE-AC05-00OR22725. Research sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Ofce of Science User Facility supported under Contract DE-AC05-00OR22725. We would like to thank the MINERvA collaboration for the use of their simulated data and for many useful and stimulating conversations. MINERvA is supported by the Fermi National Accelerator Laboratory under US Department of Energy contract No. DE-AC02-07CH11359 which included the MINERvA construction project. MINERvA construction support was also granted by the United States National Science Foundation under Award PHY-0619727 and by the University of Rochester. Support for participating MINERvA physicists was provided by NSF and DOE (USA), by CAPES and CNPq (Brazil), by CoNaCyT (Mexico), by CONICYT (Chile), by CONCYTEC, DGI-PUCP and IDI/IGIUNI (Peru), and by Latin American Center for Physics (CLAF).