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.
翻译:提取无线电频率信号中的信息,同时低能耗的利用人工神经网络实现分类,这是从雷达到健康等各种应用领域的关键需求。这些无线电输入由多个频率组成。我们展示了磁隧道结以并行方式处理带有多个频率的模拟RF输入和执行突触操作。使用一种不含反向传播的极端学习方法,我们使用磁隧道结的实验数据来对使用RF信号编码的噪声图像进行分类。我们成功地实现了与等效软件神经网络相同的准确性。这些结果是嵌入式无线电人工智能的关键一步。