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Versatile networks of simulated spiking neurons displaying winner-take-all behavior., , and . Frontiers Comput. Neurosci., (2013)11.4 IBM NorthPole: An Architecture for Neural Network Inference with a 12nm Chip., , , , , , , , , and 22 other author(s). ISSCC, page 214-215. IEEE, (2024)Learned Step Size quantization., , , , and . ICLR, OpenReview.net, (2020)Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device., and . Frontiers Neurorobotics, (2013)TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years., , , , , , , , , and 18 other author(s). Computer, 52 (5): 20-29 (2019)Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Inference., , , , , , and . EMC2@NeurIPS, page 6-9. IEEE, (2019)Active Sensing of Visual and Tactile Stimuli by Brain-based Devices., , , and . Int. J. Robotics Autom., (2004)Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing., , , , , , , and . CoRR, (2018)Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference., , , , and . CoRR, (2023)A Low Power, Fully Event-Based Gesture Recognition System., , , , , , , , , and 6 other author(s). CVPR, page 7388-7397. IEEE Computer Society, (2017)