Low Power Restricted Boltzmann Machine Using Mixed-Mode Magneto-Tunneling Junctions
Low Power Restricted Boltzmann Machine Using Mixed-Mode Magneto-Tunneling Junctions
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DOI：
10.1109/led.2018.2889881
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发表时间：
2019-02-01
影响因子：
4.9
通讯作者：
Trivedi, Amit Ranjan
中科院分区：
文献类型：
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作者：
Nasrin, Shamma;Drobitch, Justine L.;Trivedi, Amit Ranjan
这封信讨论了基于混合模式磁隧道结（m - MTJ）的受限玻尔兹曼机（RBM）。RBM是无监督学习模型，适用于从高维数据中提取特征。m - MTJ由电压控制磁各向异性和电压控制自旋转移矩的同时作用来驱动，其中自由层的翻转是概率性的，并且可由这两者控制。利用基于m - MTJ的激活函数，我们提出了一种新颖的低面积/功耗的RBM。我们讨论了所提出的实现方式的在线学习，以抵消工艺可变性。对于MNIST手写数据集，该设计在各种组件存在预期可变性的情况下达到了约96%的准确率。
This letter discusses mixed-mode magneto tunneling junction (m-MTJ)-based restricted Boltzmann machine (RBM). RBMs are unsupervised learning models, suitable for extracting features from high-dimensional data. The m-MTJ is actuated by the simultaneous actions of voltage-controlled magnetic anisotropy and voltage-controlled spin-transfer torque, where the switching of the free-layer is probabilistic and can be controlled by the two. Using m-MTJ-based activation functions, we present a novel low area/power RBM. We discuss online learning of the presented implementation to negate process variability. For MNIST hand-written dataset, the design achieves similar to 96% accuracy under expected variability in various components.