  Training of deep neural networks (DNNs) is a computationally intensive task
and requires massive volumes of data transfer. Performing these operations with
the conventional von Neumann architectures creates unmanageable time and power
costs. Recent studies have shown that mixed-signal designs involving crossbar
architectures are capable of achieving acceleration factors as high as 30,000x
over the state of the art digital processors. These approaches involve
utilization of non-volatile memory (NVM) elements as local processors. However,
no technology has been developed to-date that can satisfy the strict device
requirements for the unit cell. This paper presents the superconducting
nanowire-based processing element as a cross-point device. The unit cell has
many programmable non-volatile states that can be used to perform analog
multiplication. Importantly, these states are intrinsically discrete due to
quantization of flux, which provides symmetric switching characteristics.
Operation of these devices in a crossbar is described and verified with
electro-thermal circuit simulations. Finally, validation of the concept in an
actual DNN training task is shown using an emulator.
