  Artificial neural networks inspired by brain operations can improve the
possibilities of solving complex problems more efficiently. Today's computing
hardware, on the other hand, is mainly based on von Neumann architecture and
CMOS technology, which is inefficient at implementing neural networks. For the
first time, we propose an ultrahigh speed, spiking neuromorphic processor
architecture built upon single flux quantum (SFQ) based artificial neurons
(JJ-Neuron). Proposed architecture has the potential to provide higher
performance and power efficiency over the state of the art including CMOS,
memristors and nanophotonics devices. JJ-Neuron has the ultrafast spiking
capability, trainability with commodity design software even after fabrication
and compatibility with commercial CMOS and SFQ foundry services. We
experimentally demonstrate the soma part of the JJ-Neuron for various
activation functions together with peripheral SFQ logic gates. Then, the neural
network is trained for the IRIS dataset and we have shown 100% match with the
results of the offline training with 1.2x${10}^{10}$ synaptic operations per
second (SOPS) and 8.57x${10}^{11}$ SOPS/W performance and power efficiency,
respectively. In addition, scalability for ${10}^{18}$ SOPS and ${10}^{17}$
SOPS/W is shown which is at least five orders of magnitude more efficient than
the state of the art CMOS circuits and one order of magnitude more efficient
than estimations of nanophotonics-based architectures.
