Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systems

We present the results of circuit simulations for the adiabatic flux-operating neuron. The proposed cell with one-shot calculation of activation function is based on a modified single-junction superconducting quantum interferometer. In comparison, functionally equivalent elements of the artificial neural network (ANN) in the semiconductor-based implementations consist of approximately 20 transistors. Also in the article, we present the connecting synapse based on the adiabatic quantum flux parametron. These neurons and synapses allow constructing ANNs with a magnetic representation of information in the form of direction and/or magnitude of the magnetic flux in the superconducting circuit. We discuss the dissipation of energy during operations in the frame of the proposed concept. This value in superconducting neurons and synapses with sub-nanosecond timescale can be reduced down to 10 and 0.1 aJ, respectively. The use of the adiabatic superconducting logic circuits in our approach promises compatibility with superconducting quantum information processing systems.