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Efficient recurrent architectures through activity sparsity and sparse back-propagation through time

A. Subramoney, K. Nazeer, M. Schöne, C. Mayr, and D. Kappel. (2022)

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Squeeze-and-threshold based quantization for low-precision neural networksB. Wu, B. Waschneck, and C. Mayr. Proceedings of the International Neural Networks Society, Springer International Publishing, Cham, (2021)Comparing Loihi with a SpiNNaker 2 prototype on low-latency keyword spotting and adaptive robotic controlY. Yan, T. Stewart, X. Choo, B. Vogginger, J. Partzsch, S. Höppner, F. Kelber, C. Eliasmith, S. Furber, and C. Mayr. Neuromorph. Comput. Eng., 1 (1): 014002 (September 2021)Efficient recurrent architectures through activity sparsity and sparse back-propagation through timeA. Subramoney, K. Nazeer, M. Schöne, C. Mayr, and D. Kappel. (2022)Hardware acceleration of EEG-based emotion classification systems: A comprehensive surveyH. Gonzalez, R. George, S. Muzaffar, J. Acevedo, S. Hoppner, C. Mayr, J. Yoo, F. Fitzek, and I. Elfadel. IEEE Trans. Biomed. Circuits Syst., 15 (3): 412--442 (June 2021)Mapping deep neural networks on SpiNNaker2F. Kelber, B. Wu, B. Vogginger, J. Partzsch, C. Liu, M. Stolba, and C. Mayr. Proceedings of the Neuro-inspired Computational Elements Workshop, New York, NY, USA, ACM, (March 2020)The SpiNNaker 2 processing element architecture for hybrid digital neuromorphic computingS. Höppner, Y. Yan, A. Dixius, S. Scholze, J. Partzsch, M. Stolba, F. Kelber, B. Vogginger, F. Neumärker, G. Ellguth and 8 other author(s). (2021)SpiNNaker 2: A 10 Million core processor system for brain simulation and machine learningC. Mayr, S. Hoeppner, and S. Furber. (2019)Memory-efficient deep learning on a SpiNNaker 2 prototypeC. Liu, G. Bellec, B. Vogginger, D. Kappel, J. Partzsch, F. Neumärker, S. Höppner, W. Maass, S. Furber, R. Legenstein and 1 other author(s). Front. Neurosci., (November 2018)E-prop on SpiNNaker 2: Exploring online learning in spiking RNNs on neuromorphic hardwareA. Rostami, B. Vogginger, Y. Yan, and C. Mayr. Frontiers in Neuroscience, (2022)Applied spiking neural networks for radar-based gesture recognitionF. Kreutz, P. Gerhards, B. Vogginger, K. Knobloch, and C. Mayr. 2021 7th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP), IEEE, (June 2021)

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Disambiguation of "Mayr, Christian"

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Efficient recurrent architectures through activity sparsity and sparse back-propagation through time

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Efficient recurrent architectures through activity sparsity and sparse back-propagation through time

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Efficient recurrent architectures through activity sparsity and sparse back-propagation through time