Abstract
Parallel neuromorphic computing is based on matrix-vector multiplications which can be realized using resistor arrays. The arrays should consist of variable resistors to enable the multiplication by various matrices. The elements that can change their resistance under the action of the current flowing through them are most interesting in this case. Among different sorts of resistor array the regular crossbar structures are most manufacturable. The use of crossbar resistor arrays as storage elements or matrix-vector multipliers faces considerable difficulties. The biggest problem is how to record a needed conductance matrix with the limited number of control signals and strong inter-resistor connections. In parallel recording only specific conductance matrixes can be formed. In consecutive recording it is impossible to localize the action, not only targeted resistors change their conductance. In the paper the mathematical modelling is used to analyze the methods allowing us to generate a great variety of conductance matrices, though not permitting the formation of matrices of arbitrary types.
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The work is financially supported by State Program of SRISA RAS No. FNEF-2022–0003.
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Kotov, V.B., Beskhlebnova, G.A. (2023). Specifics of Crossbar Resistor Arrays. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research VI. NEUROINFORMATICS 2022. Studies in Computational Intelligence, vol 1064. Springer, Cham. https://doi.org/10.1007/978-3-031-19032-2_31
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DOI: https://doi.org/10.1007/978-3-031-19032-2_31
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