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A differential memristive synapse circuit for on-line learning in neuromorphic computing systems


Nair, Manu V; Muller, Lorenz K; Indiveri, Giacomo (2017). A differential memristive synapse circuit for on-line learning in neuromorphic computing systems. Nano Futures, (1):035003.

Abstract

Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes. This imposes severe constraints on the length of the pulses transmitted in the network, and on the network's throughput. Furthermore, most of these circuits do not decouple the currents flowing through memristive devices from the one stimulating the target neuron. This can be a problem when using devices with high conductance values, because of the resulting large currents. In this paper, we propose a novel circuit that decouples the current produced by the memristive device from the one used to stimulate the post-synaptic neuron, by using a novel differential scheme based on the Gilbert normalizer circuit. We show how this circuit is useful for reducing the effect of variability in the memristive devices, and how it is ideally suited for spike-based learning mechanisms that do not require overlapping pre- and post-synaptic pulses. We demonstrate the features of the proposed synapse circuit with SPICE simulations, and validate its learning properties with high-level behavioral network simulations which use a stochastic gradient descent learning rule in two benchmark classification tasks.

Abstract

Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes. This imposes severe constraints on the length of the pulses transmitted in the network, and on the network's throughput. Furthermore, most of these circuits do not decouple the currents flowing through memristive devices from the one stimulating the target neuron. This can be a problem when using devices with high conductance values, because of the resulting large currents. In this paper, we propose a novel circuit that decouples the current produced by the memristive device from the one used to stimulate the post-synaptic neuron, by using a novel differential scheme based on the Gilbert normalizer circuit. We show how this circuit is useful for reducing the effect of variability in the memristive devices, and how it is ideally suited for spike-based learning mechanisms that do not require overlapping pre- and post-synaptic pulses. We demonstrate the features of the proposed synapse circuit with SPICE simulations, and validate its learning properties with high-level behavioral network simulations which use a stochastic gradient descent learning rule in two benchmark classification tasks.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2017
Deposited On:01 Mar 2018 11:43
Last Modified:19 Aug 2018 14:47
Publisher:IOP Publishing
ISSN:2399-1984
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1088/2399-1984/aa954a
Official URL:http://iopscience.iop.org/article/10.1088/2399-1984/aa954a/meta
Project Information:
  • : FunderSNSF
  • : Grant IDCRSII2_160756
  • : Project TitleHybrid CMOS/Memristive Neuromorphic Systems for Data Analytics
  • : FunderH2020
  • : Grant ID687299
  • : Project TitleNeuRAM3 - NEUral computing aRchitectures in Advanced Monolithic 3D-VLSI nano-technologies

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