Digital-analog concept for superconducting perceptron-like neural networks

A promising route to superconducting artificial neural networks is a hybrid digital-analog architecture that combines digital single-flux-quantum (SFQ) communication with compact analog nonlinear processing. The study focused on the dynamic conversion of a discrete signal passing through a digital-to-analog-to-digital (DAD) converter, in which the role of the analog cell was performed by a $\mathrm{\ensuremath{\Sigma}}$-neuron with a nonlinear transfer function---the basic cell of perceptron-like neural networks. Furthermore, the DAD converter, the elementary functional block of the hybrid architecture, combines a digital-to-analog converter (DAC) and an analog-to-digital converter (ADC), and reencodes the analog $\mathrm{\ensuremath{\Sigma}}$-neuron wave forms as an SFQ pulse sequence. Circuit-level simulations demonstrate how input values encoded by SFQ pulse trains are converted into analog signal levels, transformed by the $\mathrm{\ensuremath{\Sigma}}$-neuron, and mapped back to p