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：利用人工神经网络以低能耗从射频信号中提取信息是雷达、健康等领域广泛应用的迫切需求。射频输入由多个频率组成。本文证明磁隧道结能够并行处理包含多个频率的模拟射频输入并执行突触运算。通过采用一种名为极端学习的无反向传播方法，我们利用同时作为突触和神经元的磁隧道结的实验数据，对由射频信号编码的含噪图像进行分类。最终达到了与等效软件神经网络相同的准确率。这些成果是实现嵌入式射频人工智能的关键一步。