Domain Wall Leaky Integrate-and-Fire Neurons With Shape-Based Configurable Activation Functions
Domain Wall Leaky Integrate-and-Fire Neurons With Shape-Based Configurable Activation Functions
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DOI：
10.1109/ted.2022.3159508
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发表时间：
2022-03-28
影响因子：
3.1
通讯作者：
Friedman, Joseph S.
中科院分区：
文献类型：
--
作者：
Brigner, Wesley H.;Hassan, Naimul;Friedman, Joseph S.
CMOS器件显示易失性特性，不适合模拟应用，如神经形态计算。另一方面，自旋电子器件表现出非易失性和模拟特性，非常适合神经形态计算。因此，这些新型器件处于超越cmos人工智能应用的最前沿。然而，大量这些人工神经形态器件仍然需要使用CMOS来实现各种神经形态功能，这降低了系统的效率。为了解决这个问题，我们之前提出了一些不需要CMOS操作的人工神经元和突触。虽然这些设备比以前的版本有了很大的改进，但它们使神经网络学习和识别的能力受到其内在激活功能的限制。这项工作提出了对这些自旋电子神经元的修改，通过控制磁畴壁轨迹的形状来实现激活功能的配置。在这项工作中展示了线性和s型激活函数，它们可以通过类似的方法进行扩展，以实现各种各样的激活函数。
CMOS devices display volatile characteristics and are not well suited for analog applications such as neuromorphic computing. Spintronic devices, on the other hand, exhibit both non-volatile and analog features, which are well suited to neuromorphic computing. Consequently, these novel devices are at the forefront of beyond-CMOS artificial intelligence applications. However, a large quantity of these artificial neuromorphic devices still require the use of CMOS to implement various neuromorphic functionalities, which decreases the efficiency of the system. To resolve this, we have previously proposed a number of artificial neurons and synapses that do not require CMOS for operation. Although these devices are a significant improvement over previous renditions, their ability to enable neural network learning and recognition is limited by their intrinsic activation functions. This work proposes modifications to these spintronic neurons that enable configuration of the activation functions through control of the shape of a magnetic domain wall track. Linear and sigmoidal activation functions are demonstrated in this work, which can be extended through a similar approach to enable a wide variety of activation functions.