Toomey E, Segall K, Castellani M, Colangelo M, Lynch N, Berggren K K
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Department of Physics and Astronomy, Colgate University, Hamilton, New York 13346, United States.
Nano Lett. 2020 Nov 11;20(11):8059-8066. doi: 10.1021/acs.nanolett.0c03057. Epub 2020 Oct 7.
As the limits of traditional von Neumann computing come into view, the brain's ability to communicate vast quantities of information using low-power spikes has become an increasing source of inspiration for alternative architectures. Key to the success of these largescale neural networks is a power-efficient spiking element that is scalable and easily interfaced with traditional control electronics. In this work, we present a spiking element fabricated from superconducting nanowires that has pulse energies on the order of ∼10 aJ. We demonstrate that the device reproduces essential characteristics of biological neurons, such as a refractory period and a firing threshold. Through simulations using experimentally measured device parameters, we show how nanowire-based networks may be used for inference in image recognition and that the probabilistic nature of nanowire switching may be exploited for modeling biological processes and for applications that rely on stochasticity.
随着传统冯·诺依曼计算的局限性逐渐显现，大脑利用低功耗脉冲来传递大量信息的能力，越来越多地启发人们开发替代架构。这些大规模神经网络成功的关键在于一种节能的脉冲元件，它具有可扩展性，并且能轻松与传统控制电子设备相连接。在这项工作中，我们展示了一种由超导纳米线制成的脉冲元件，其脉冲能量约为10阿焦耳。我们证明，该器件再现了生物神经元的基本特征，比如不应期和激发阈值。通过使用实验测量的器件参数进行模拟，我们展示了基于纳米线的网络如何用于图像识别推理，以及纳米线开关的概率特性如何用于模拟生物过程和依赖随机性的应用。
