Afshari Sahra, Xie Jing, Musisi-Nkambwe Mirembe, Radhakrishnan Sritharini, Sanchez Esqueda Ivan
Department of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, 85281, United States of America.
Nanotechnology. 2023 Aug 17;34(44). doi: 10.1088/1361-6528/acebf5.
Resistive random access memory (RRAM) is an emerging non-volatile memory technology that can be used in neuromorphic computing hardware to exceed the limitations of traditional von Neumann architectures by merging processing and memory units. Two-dimensional (2D) materials with non-volatile switching behavior can be used as the switching layer of RRAMs, exhibiting superior behavior compared to conventional oxide-based devices. In this study, we investigate the electrical performance of 2D hexagonal boron nitride (h-BN) memristors towards their implementation in spiking neural networks (SNN). Based on experimental behavior of the h-BN memristors as artificial synapses, we simulate the implementation of unsupervised learning in SNN for image classification on the Modified National Institute of Standards and Technology dataset. Additionally, we propose a simple spike-timing-dependent-plasticity (STDP)-based dropout technique to enhance the recognition rate in h-BN memristor-based SNN. Our results demonstrate the viability of using 2D-material-based memristors as artificial synapses to perform unsupervised learning in SNN using hardware-friendly methods for online learning.
电阻式随机存取存储器（RRAM）是一种新兴的非易失性存储技术，可用于神经形态计算硬件，通过合并处理和存储单元来突破传统冯·诺依曼架构的限制。具有非易失性开关行为的二维（2D）材料可作为RRAM的开关层，与传统的氧化物基器件相比，表现出更优异的性能。在本研究中，我们研究了二维六方氮化硼（h-BN）忆阻器在脉冲神经网络（SNN）中的电学性能及其实现方式。基于h-BN忆阻器作为人工突触的实验行为，我们模拟了在改进的国家标准与技术研究所数据集上，SNN中用于图像分类的无监督学习的实现。此外，我们提出了一种基于简单的脉冲时间依赖可塑性（STDP）的随机失活技术，以提高基于h-BN忆阻器的SNN的识别率。我们的结果证明了使用基于二维材料的忆阻器作为人工突触，通过硬件友好的在线学习方法在SNN中执行无监督学习的可行性。
