论文标题
更新和基因训练算法的比较
Comparison of Update and Genetic Training Algorithms in a Memristor Crossbar Perceptron
论文作者
论文摘要
基于Memristor的计算机架构越来越有吸引力，作为用于实现神经网络的硬件的可能选择。但是，目前，Memristor Technologies容易受到各种故障模式的影响，这在任何应用程序都可能无法预期甚至可能无法期望甚至可能。在这项研究中，我们研究了某些培训算法是否可能对特定的硬件故障模式更具弹性，因此更适合在这些应用中使用。我们在模拟的Memristor Crossbar中实现了两种培训算法 - 一种本地更新方案和一种遗传算法，并将其训练用于简单图像分类任务的能力进行比较，因为越来越多的Memristors无法调整其电导率。我们证明，两种算法之间在几种训练率的措施中，两种算法之间存在明显的区别。
Memristor-based computer architectures are becoming more attractive as a possible choice of hardware for the implementation of neural networks. However, at present, memristor technologies are susceptible to a variety of failure modes, a serious concern in any application where regular access to the hardware may not be expected or even possible. In this study, we investigate whether certain training algorithms may be more resilient to particular hardware failure modes, and therefore more suitable for use in those applications. We implement two training algorithms -- a local update scheme and a genetic algorithm -- in a simulated memristor crossbar, and compare their ability to train for a simple image classification task as an increasing number of memristors fail to adjust their conductance. We demonstrate that there is a clear distinction between the two algorithms in several measures of the rate of failure to train.
