Probabilistic neuromorphic system using binary phase-change memory (PCM) synapses: Detailed power consumption analysis

Abstract : In this paper we investigate the use of phase-change memory (PCM) devices as binary probabilistic synapses in a neuromorphic computing system for complex visual pattern extraction. Different PCM programming schemes for architectures with- or without-selector devices are provided. The functionality of the system is tested through large-scale neural network simulations. The system-level simulations show that such a system can solve a complex real-life video processing problem (vehicle counting) with high recognition rate (>94%) and low power consumption. The impact of the resistance window on the power consumption of the system is also studied. Results show that the learning-mode power consumption can be dramatically reduced if the RESET state of the PCM devices is tuned to a relatively low resistance. Read-mode power consumption, on the other hand, can be minimized by increasing the resistance values for both SET and RESET states of the PCM devices.
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https://hal-cea.archives-ouvertes.fr/cea-01839868
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Submitted on : Monday, July 16, 2018 - 10:06:38 AM
Last modification on : Monday, February 25, 2019 - 4:34:21 PM

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D. Garbin, M. Suri, O. Bichler, D. Querlioz, C. Gamrat, et al.. Probabilistic neuromorphic system using binary phase-change memory (PCM) synapses: Detailed power consumption analysis. 2013 13th IEEE International Conference on Nanotechnology (IEEE-NANO 2013), Aug 2013, Beijing, China. pp.91-94, ⟨10.1109/NANO.2013.6721057⟩. ⟨cea-01839868⟩

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