A Spiking Recurrent Neural Network with Phase Change Memory Synapses for Decision Making

G. Pedretti, V. Milo, S. Hashemkhani, P. Mannocci, O. Melnic, E. Chicca, D. Ielmini

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

Abstract

Neuronal activity of recurrent neural networks (RNNs) experimentally observed in the hippocampus is widely believed to play a key role for mammalian ability to associate concepts and make decisions. For this reason, RNNs have rapidly gained strong interest as computational enabler of brain-inspired cognitive functions in hardware. From the technology viewpoint, nonvolatile memory devices such as phase change memory (PCM) and resistive switching memory (RRAM) have become a key asset to allow for high synaptic density and biorealistic cognitive functionality. In this work, we demonstrate for the first time associative learning and decision making in a hardware Hopfield RNN with 6 spiking neurons and PCM synapses via storage, recall and competition of attractor states. We also experimentally demonstrate the solution of a constraint satisfaction problem (CSP) namely a Sudoku with size 2×2 in hardware and 9×9 in simulation. These results support spiking RNNs with PCM devices for the implementation of decision making capabilities in hardware neuromorphic systems.
Original languageEnglish
Title of host publication2020 IEEE International Symposium on Circuits and Systems (ISCAS)
PublisherIEEE
Number of pages5
DOIs
Publication statusPublished - 2020
Externally publishedYes

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