Kumar Ashwani, Goteti Uday S, Cubukcu Ertugrul, Dynes Robert C, Kuzum Duygu
Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, United States.
Department of Physics, University of California San Diego, San Diego, CA, United States.
Front Neurosci. 2025 Feb 14;19:1511371. doi: 10.3389/fnins.2025.1511371. eCollection 2025.
With Moore's law nearing its end due to the physical scaling limitations of CMOS technology, alternative computing approaches have gained considerable attention as ways to improve computing performance. Here, we evaluate performance prospects of a new approach based on disordered superconducting loops with Josephson-junctions for energy efficient neuromorphic computing. Synaptic weights can be stored as internal trapped fluxon states of three superconducting loops connected with multiple Josephson-junctions (JJ) and modulated by input signals applied in the form of discrete fluxons (quantized flux) in a controlled manner. The stable trapped fluxon state directs the incoming flux through different pathways with the flow statistics representing different synaptic weights. We explore implementation of matrix-vector-multiplication (MVM) operations using arrays of these fluxon synapse devices. We investigate the energy efficiency of online-learning of MNIST dataset. Our results suggest that the fluxon synapse array can provide ~100× reduction in energy consumption compared to other state-of-the-art synaptic devices. This work presents a proof-of-concept that will pave the way for development of high-speed and highly energy efficient neuromorphic computing systems based on superconducting materials.
由于CMOS技术的物理缩放限制，摩尔定律即将走到尽头，替代计算方法作为提高计算性能的途径已受到广泛关注。在此，我们评估一种基于具有约瑟夫森结的无序超导环的新方法在节能神经形态计算方面的性能前景。突触权重可以存储为与多个约瑟夫森结（JJ）相连的三个超导环的内部俘获磁通子状态，并由以离散磁通子（量化磁通）形式施加的输入信号以可控方式进行调制。稳定的俘获磁通子状态引导入射磁通通过不同路径，其流动统计表示不同的突触权重。我们探索使用这些磁通子突触器件阵列实现矩阵-向量乘法（MVM）运算。我们研究MNIST数据集在线学习的能量效率。我们的结果表明，与其他先进的突触器件相比，磁通子突触阵列可将能耗降低约100倍。这项工作提供了一个概念验证，将为基于超导材料的高速、高能效神经形态计算系统的开发铺平道路。
