A Twin Memristor Synapse for Spike Timing Dependent Learning in Neuromorphic Systems

Adnan, Md Musabbir; Sayyaparaju, Sagarvarma; Rose, Garrett; Schuman, Catherine; Woong Ku, Bon; Kyu Lim, Sung

doi:10.1109/SOCC.2018.8618553

Title: A Twin Memristor Synapse for Spike Timing Dependent Learning in Neuromorphic Systems

Conference · Tue Jan 01 00:00:00 EST 2019

DOI:https://doi.org/10.1109/SOCC.2018.8618553· OSTI ID:1492171

Adnan, Md Musabbir ^[1]; Sayyaparaju, Sagarvarma ^[1]; Rose, Garrett ^[1];

^[2]; Woong Ku, Bon ^[3]; Kyu Lim, Sung ^[3]

University of Tennessee (UT)
ORNL
Georgia Institute of Technology

Neuromorphic systems consist of a framework of spiking neurons interconnected via plastic synaptic junctures. The discovery of a two terminal passive nanoscale memristive device has spurred great interest in the realization of memristive plastic synapses in neural networks. In this work, a synapse structure is presented that utilizes a pair of memristors, to implement both positive and negative weights. The working scheme of this synapse as an electrical interlink between neurons is explained, and the relative timing of their spiking events is analyzed, which leads to a modulation of the synaptic weight in accordance with the spike-timing-dependent plasticity (STDP) rule. A digital pulse width modulation technique is proposed to achieve these variable changes to the synaptic weight. The synapse architecture presented is shown to have high accuracy when used in neural networks for classification tasks. Lastly, the energy requirement of the system during various phases of operation is presented.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-00OR22725

OSTI ID:: 1492171

Resource Relation:: Conference: IEEE International System-on-Chip Conference (SOCC) - Arlington, Virginia, United States of America - 9/4/2018 4:00:00 AM-9/7/2018 4:00:00 AM

Country of Publication:: United States

Language:: English

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