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Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing.

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Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device., and . Front. Neurorobot., (2013)Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing., , , , , , , , , and 6 other author(s). CoRR, (2016)Versatile networks of simulated spiking neurons displaying winner-take-all behavior., , and . Front. Comput. Neurosci., (2013)TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years., , , , , , , , , and 18 other author(s). IEEE Computer, 52 (5): 20-29 (2019)Structured Convolution Matrices for Energy-efficient Deep learning., , , , , , , , and . CoRR, (2016)Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing., , , , , , , and . CoRR, (2018)Active Sensing of Visual and Tactile Stimuli by Brain-based Devices., , , and . I. J. Robotics and Automation, (2004)Texture Discrimination by an Autonomous Mobile Brain-based Device with Whiskers., , , and . ICRA, page 4925-4930. IEEE, (2004)Testing for Machine Consciousness Using Insight Learning., and . AAAI Fall Symposium: AI and Consciousness, volume FS-07-01 of AAAI Technical Report, page 103-108. AAAI Press, (2007)Learned Step Size Quantization., , , , and . CoRR, (2019)