A current-mode CMOS/memristor hybrid implementation of an extreme learning machine
A current-mode CMOS/memristor hybrid implementation of an extreme learning machine
复制标题
极限学习机的电流模式 CMOS/忆阻器混合实现
DOI：
10.1145/2591513.2591572
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发表时间：
2014
影响因子：
4
通讯作者：
D. Kudithipudi
中科院分区：
文献类型：
--
作者：
Cory E. Merkel;D. Kudithipudi
在这项工作中，我们提出了一种电流模式CMOS/忆阻器混合实现的极限学习机（ELM）架构。我们提出了线性、s型和阈值神经元激活函数的新电路设计，以及基于记忆电阻器的双极突触加权。此外，这项工作提出了一种随机版本的最小均方（LMS）训练算法，用于适应ELM的隐藏层和输出层之间的权重。我们使用Cadence AMS Designer模拟了我们的顶层ELM架构，采用45纳米CMOS模型和基于HfOx器件实验数据的经验分段线性忆阻器模型。通过10个隐藏节点神经元，ELM能够在150次训练后学习一个2输入异或函数。
In this work, we propose a current-mode CMOS/memristor hybrid implementation of an extreme learning machine (ELM) architecture. We present novel circuit designs for linear, sigmoid,and threshold neuronal activation functions, as well as memristor-based bipolar synaptic weighting. In addition, this work proposes a stochastic version of the least-mean-squares (LMS) training algorithm for adapting the weights between the ELM's hidden and output layers. We simulated our top-level ELM architecture using Cadence AMS Designer with 45 nm CMOS models and an empirical piecewise linear memristor model based on experimental data from an HfOx device. With 10 hidden node neurons, the ELM was able to learn a 2-input XOR function after 150 training epochs.