论文标题
通过极端学习将多频RF信号分类，使用磁性隧道连接作为神经元和突触
Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses
论文作者
论文摘要
使用人工神经网络以低能成本从射频（RF）信号中提取信息是对从雷达到健康的广泛应用的至关重要的。这些RF输入由倍频频率组成。在这里，我们表明磁性隧道连接可以在平行的多个频率并执行突触操作的情况下处理模拟RF输入。使用称为“极限学习”的无反向传播方法，我们使用来自RF信号编码的嘈杂图像，使用来自磁性隧道连接的实验数据，既可以突触和神经元。我们达到与等效软件神经网络相同的精度。这些结果是嵌入式射频人工智能的关键步骤。
Extracting information from radiofrequency (RF) signals using artificial neural networks at low energy cost is a critical need for a wide range of applications from radars to health. These RF inputs are composed of multiples frequencies. Here we show that magnetic tunnel junctions can process analogue RF inputs with multiple frequencies in parallel and perform synaptic operations. Using a backpropagation-free method called extreme learning, we classify noisy images encoded by RF signals, using experimental data from magnetic tunnel junctions functioning as both synapses and neurons. We achieve the same accuracy as an equivalent software neural network. These results are a key step for embedded radiofrequency artificial intelligence.
