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In‐Memory Binary Vector–Matrix Multiplication Based on Complementary Resistive Switches

Advanced Intelligent Systems, vol. 2

Abstract

Binary Neural Networks In article number 2000134, Stephan Menzel and co-workers explore a computation in-memory concept for binary vector-matrix multiplications based on complementary resistive switches. Experimental results on a small-scale demonstrator are shown and the influence of device variability is investigated. The simulated inference of a 1-layer fully connected binary neural network trained on the MNIST data set resulted in an accuracy of nearly 86%.

Authors 4

  1. RWTH Aachen University · Jülich Aachen Research Alliance

    Affiliation as printed

    JARA-FIT and Institute of Materials in Electrical Engineering and Information Technology II RWTH Aachen University Sommerfeldstraße 24 Aachen 52074 Germany

  2. RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance

    Affiliation as printed

    JARA-FIT and Institute of Materials in Electrical Engineering and Information Technology II RWTH Aachen University Sommerfeldstraße 24 Aachen 52074 Germany

    JARA-FIT and Peter Grünberg Institute 10 Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße Jülich 52428 Germany

    JARA-FIT and Peter Grünberg Institute 7 Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße Jülich 52428 Germany

  3. RWTH Aachen University · Jülich Aachen Research Alliance

    Affiliation as printed

    JARA-FIT and Institute of Materials in Electrical Engineering and Information Technology II RWTH Aachen University Sommerfeldstraße 24 Aachen 52074 Germany

  4. Stephan Menzel corresponding

    Forschungszentrum Jülich · Jülich Aachen Research Alliance

    Affiliation as printed

    JARA-FIT and Peter Grünberg Institute 7 Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße Jülich 52428 Germany

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