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Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks.

, , , and . CoRR, (2017)

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Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks., , , and . CoRR, (2017)Boltzmann Energetics and Temporal Dynamics of Learning Neuromorphic Systems.. University of California, San Diego, USA, (2019)base-search.net (ftcdlib:qt5d83n1x2).Unsupervised Synaptic Pruning Strategies for Restricted Boltzmann Machines., , , , and . BioCAS, page 1-4. IEEE, (2018)Fast classification using sparsely active spiking networks., , , and . ISCAS, page 1-4. IEEE, (2017)Neuromorphic adaptations of restricted Boltzmann machines and deep belief networks., , , , and . IJCNN, page 1-6. IEEE, (2013)TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth., , , , , and . CoRR, (2016)Output stage based high-resolution min/max and rank-order filters., and . IEEE Trans. Circuits Syst. II Express Briefs, 52-II (1): 28-32 (2005)Dropout and DropConnect for Reliable Neuromorphic Inference Under Communication Constraints in Network Connectivity., , , , , and . IEEE J. Emerg. Sel. Topics Circuits Syst., 9 (4): 658-667 (2019)Design Principles of Large-Scale Neuromorphic Systems Centered on High Bandwidth Memory., , , and . ICRC, page 90-94. IEEE, (2020)Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware., , , , and . ICRC, page 1-8. IEEE Computer Society, (2016)