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arXiv · 2211.01131

Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses

Nathan Leroux·Danijela Marković·Dédalo Sanz-Hernández·Juan Trastoy·Paolo Bortolotti·Alejandro Schulman·Luana Benetti·Alex Jenkins·Ricardo Ferreira·Julie Grollier·Alice Mizrahi
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Abstract

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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Nathan Leroux, Danijela Marković, Dédalo Sanz-Hernández, Juan Trastoy, Paolo Bortolotti, Alejandro Schulman, Luana Benetti, Alex Jenkins, Ricardo Ferreira, Julie Grollier, Alice Mizrahi. 2022-11-02. Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses. https://arxiv.org/abs/2211.01131

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