Abstract
Variable resistors have proven to be potential elements of massively parallel computation systems. Non-resistor components (transistors, diodes, capacitors) are indispensible is such systems. However it might be desirable to integrate only resistor elements into large systems at the first stage of integration. The question arises of the adequate data representation in this sort of systems. In this connection, a system totally consisted of variable resistors located at junctions of conductors is considered. The use of individual resistors as data medium is shown to be impracticable because of nonlocality of the data recording process. The use of group properties of a conductance matrix looks more promising. It is convenient to take a representative graph of the conductance matrix as data-encoding entities. Being the result of the thinning of the matrix by zeroing small conductivities, this sort of graph is noise resistant and can determine the effective two-terminal conductivity to good accuracy. The dynamics of the representative graph is defined by the interaction between initial conditions, the signature graph and input signals.
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Kotov, V.B., Beskhlebnova, G.A. (2021). Data Representation in All-Resistor Systems. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research IV. NEUROINFORMATICS 2020. Studies in Computational Intelligence, vol 925. Springer, Cham. https://doi.org/10.1007/978-3-030-60577-3_39
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DOI: https://doi.org/10.1007/978-3-030-60577-3_39
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