Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses
This work addresses low-energy RF signal processing for applications like radars and health, representing an incremental advance in embedded radiofrequency AI.
The paper tackled the problem of classifying multi-frequency RF signals using magnetic tunnel junctions as neurons and synapses with extreme learning, achieving the same accuracy as an equivalent software neural network.
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.