Neuromorphic computing has the potential to further the success of software-based artificial neural networks (ANNs) by designing hardware from a different perspective. Current research in neuromorphic hardware targets dramatic improvements to ANN performance by increasing energy efficiency, speed of operation, and even seeks to extend the utility of ANNs by natively adding functionality such as spiking operation. One promising neuromorphic hardware platform is based on superconductive electronics, which has the potential to incorporate all of these advantages at the device level in addition to offering the potential of near lossless communications both within the neuromorphic circuits as well as between disparate superconductive chips. Here we explore one of the fundamental brain-inspired architecture components, the fan-in and fan-out as realized in superconductive circuits based on Josephson junctions. From our calculations and WRSPICE simulations we find that the fan-out should be limited only by junction count and circuit size limitations, and we demonstrate results in simulation at a level of 1-to-10,000, similar to that of the human brain. We find that fan-in has more limitations, but a fan-in level on the order of a few 100-to-1 should be achievable based on current technology. We discuss our findings and the critical parameters that set the limits on fan-in and fan-out in the context of superconductive neuromorphic circuits.

中文翻译：

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超导神经形态电路的扇出和扇入特性

通过从不同的角度设计硬件，神经形态计算有可能进一步推动基于软件的人工神经网络 (ANN) 的成功。当前对神经形态硬件的研究旨在通过提高能源效率、操作速度来显着提高 ANN 的性能，甚至试图通过本机添加诸如尖峰操作之类的功能来扩展 ANN 的效用。一种有前途的神经形态硬件平台基于超导电子学，除了在神经形态电路内以及不同的超导芯片之间提供近乎无损的通信潜力之外，它还有可能在设备级别结合所有这些优势。在这里，我们探索了受大脑启发的基本架构组件之一，在基于约瑟夫森结的超导电路中实现的扇入和扇出。从我们的计算和 WRSPICE 模拟中，我们发现扇出应该仅受结数和电路尺寸限制，并且我们展示了 1 到 10,000 级的模拟结果，类似于人脑的结果。我们发现扇入有更多限制，但基于当前技术，大约 100 比 1 的扇入水平应该是可以实现的。我们讨论了我们的发现以及在超导神经形态电路的背景下设置扇入和扇出限制的关键参数。我们在模拟中展示了 1 到 10,000 的结果，类似于人脑的结果。我们发现扇入有更多限制，但基于当前技术，大约 100 比 1 的扇入水平应该是可以实现的。我们讨论了我们的发现以及在超导神经形态电路的背景下设置扇入和扇出限制的关键参数。我们在模拟中展示了 1 到 10,000 的结果，类似于人脑的结果。我们发现扇入有更多限制，但基于当前技术，大约 100 比 1 的扇入水平应该是可以实现的。我们讨论了我们的发现以及在超导神经形态电路的背景下设置扇入和扇出限制的关键参数。