Using simulation to define the tolerances for the information and physical parameters of memristors-based artificial neural networks

Abstract

The article covers a solution to a problem of defining the tolerances of information and physical parameters of components of artificial neural networks (ANNs), which are implemented as hardware through the application of nanoscale electronic components with memristive properties (memristors). The developed method foundation is a system approach to the memristors-based ANN (ANNM) design, whereby the ANNMs should be studied as united physical and informational objects. When the ANNM is produced and operated, the errors of its components' physical parameters provoke information parameter errors. To define the tolerated errors (tolerances), a simulation methodology is used. The potential of the developed method is illustrated through the process of defining tolerances for the synaptic weights and neural biases of a two-layer feed forward ANNM.