基于磁性隧道结的群体编码实现无监督聚类  

Implementation of unsupervised clustering based on population coding of magnetic tunnel junctions

作　　者：张亚君 蔡佳林 乔亚 曾中明[2] 袁喆 夏钶 Zhang Ya-Jun;Cai Jia-Lin;Qiao Ya;Zeng Zhong-Ming;Yuan Zhe;Xia Ke(Center for Advanced Quantum Studies,Department of Physics,Beijing Normal University,Beijing 100875,China;Suzhou Institute of Nano-Tech and Nano-Bionics,Chinese Academy of Sciences,Suzhou 215123,China;Beijing Computational Science Research Center,Beijing 100193,China)

机构地区：[1]北京师范大学物理学系,高等量子研究中心,北京100875 [2]中国科学院苏州纳米技术与纳米仿生研究所,苏州215123 [3]北京计算科学研究中心,北京100193

出　　处：《物理学报》2022年第14期388-395,共8页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:11734004,12174028)资助的课题.

摘　　要：利用新型材料器件发展类脑计算硬件研究的关键问题是发展出合适的算法,能够发挥新器件的特点和优势.群体编码是生物神经系统常见的编码方式,能够有效去除噪音,实现短时程记忆及复杂的非线性映射功能.本文选择自旋电子学器件中研究较多、工艺较成熟的磁性隧道结,应用其可调控的随机动力学实现群体编码.作为一个应用的例子,超顺磁隧道结构建的二层脉冲神经网络成功完成了鸢尾花数据集的无监督聚类.数值仿真表明基于磁性隧道结的群体编码可以有效对抗器件的非均一性,为类脑计算硬件研究提供重要的参考.Developing suitable algorithms that utilize the natural advantages of the corresponding devices is a key issue in the hardware research of brain-inspired computing. Population coding is one of the computational schemes in biological neural systems and it contains the mechanisms for noise reduction, short-term memory and implementation of complex nonlinear functions. Here we show the controllable stochastic dynamical behaviors for the technically mature spintronic device, magnetic tunnel junctions, which can be used as the basis of population coding. As an example, we construct a two-layer spiking neural network, in which groups of magnetic tunnel junctions are used to code input data. After unsupervised learning, this spiking neural network successfully classifies the iris data set. Numerical simulation demonstrates that the population coding is robust enough against the nonuniform dispersion in devices, which is inevitable in fabrication and integration of hardware devices.

关 键 词：磁性隧道结 群体编码 脉冲神经网络 无监督学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]