  Conventional semiconductor-based integrated circuits are gradually
approaching fundamental scaling limits. Many prospective solutions have
recently emerged to supplement or replace both the technology on which basic
devices are built and the architecture of data processing. Neuromorphic
circuits are a promising approach to computing where techniques used by the
brain to achieve high efficiency are exploited. Many existing neuromorphic
circuits rely on unconventional and useful properties of novel technologies to
better mimic the operation of the brain. One such technology is single flux
quantum (SFQ) logic -- a cryogenic superconductive technology in which the data
are represented by quanta of magnetic flux (fluxons) produced and processed by
Josephson junctions embedded within inductive loops. The movement of a fluxon
within a circuit produces a quantized voltage pulse (SFQ pulse), resembling a
neuronal spiking event. These circuits routinely operate at clock frequencies
of tens to hundreds of gigahertz, making SFQ a natural technology for
processing high frequency pulse trains.
  Prior proposals for SFQ neural networks often require energy-expensive fluxon
conversions, involve heterogeneous technologies, or exclusively focus on device
level behavior. In this paper, a design methodology for deep single flux
quantum neuromorphic networks is presented. Synaptic and neuronal circuits
based on SFQ technology are presented and characterized. Based on these
primitives, a deep neuromorphic XOR network is evaluated as a case study, both
at the architectural and circuit levels, achieving wide classification margins.
The proposed methodology does not employ unconventional superconductive devices
or semiconductor transistors. The resulting networks are tunable by an external
current, making this proposed system an effective approach for scalable
cryogenic neuromorphic computing.
