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.

传统半导体集成电路正逐渐接近基础尺度极限。近期出现了许多潜在解决方案来补充或替代基础设备所构建的技术和数据处理架构。神经形态电路是一种有前途的计算方法，利用大脑实现高效性的技术。许多现有的神经形态电路依赖于新技术的非凡和有用性质来更好地模拟大脑的运作。本文介绍了一种用于深度单通量量子神经形态网络的设计方法。基于 SFQ 技术的突触和神经元电路被提出并表征。基于这些基本单元，通过在体系结构和电路级别上进行评估，实现了广泛的分类边界。该方法不使用非传统超导设备或半导体晶体管。由外部电流调整的结果网络使得该系统成为可扩展的低温神经形态计算的有效方法。