Neural networks and neuromorphic computing play pivotal roles in deep learning and machine vision. Due to their dissipative nature and inherent limitations, traditional semiconductor-based circuits face challenges in realizing ultra-fast and low-power neural networks. However, the spiking behavior characteristic of single flux quantum (SFQ) circuits positions them as promising candidates for spiking neural networks (SNNs). Our previous work showcased a JJ-Soma design capable of operating at tens of gigahertz while consuming only a fraction of the power compared to traditional circuits, as documented in [1]. This paper introduces a compact SFQ-based synapse design that applies positive and negative weighted inputs to the JJ-Soma. Using an RSFQ synapse empowers us to replicate the functionality of a biological neuron, a crucial step in realizing a complete SNN. The JJ-Synapse can operate at ultra-high frequencies, exhibits orders of magnitude lower power consumption than CMOS counterparts, and can be conveniently fabricated using commercial Nb processes. Furthermore, the network's flexibility enables modifications by incorporating cryo-CMOS circuits for weight value adjustments. In our endeavor, we have successfully designed, fabricated, and partially tested the JJ-Synapse within our cryocooler system. Integration with the JJ-Soma further facilitates the realization of a high-speed inference SNN.

翻译：神经网络和神经形态计算在深度学习与机器视觉中发挥着关键作用。由于其耗散特性和固有局限性，传统半导体电路在实现超快、低功耗神经网络方面面临挑战。然而，单通量量子（SFQ）电路特有的脉冲行为使其成为脉冲神经网络（SNN）的理想候选方案。我们前期工作提出了一种能在数十千兆赫兹频率下运行、功耗仅为传统电路极小部分的超导结体（JJ-Soma）设计（详见文献[1]）。本文介绍了一种紧凑型基于SFQ的突触设计，该设计可向超导结体施加正负加权输入。通过使用RSFQ突触，我们能够复制生物神经元的功能，这是实现完整SNN的关键步骤。超导结突触（JJ-Synapse）可在超高频下工作，功耗比CMOS同类电路低数个数量级，且可通过商用铌工艺便捷制造。此外，该网络的灵活性允许通过集成低温CMOS电路调整权重值进行修改。在我们的研究中，已成功在低温冷却系统中设计、制造并部分测试了超导结突触。与超导结体的集成进一步推动了高速推理SNN的实现。