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Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses. (arXiv:2211.01131v2 [cond-mat.mes-hall] UPDATED)

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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.

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