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

• URL: http://arxiv.org/abs/2211.01131v2
• Date: Thu, 20 Apr 2023 12:10:00 GMT
• Title: Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses
• Authors: Nathan Leroux, Danijela Markovi\'c, D\'edalo Sanz-Hern\'andez, Juan Trastoy, Paolo Bortolotti, Alejandro Schulman, Luana Benetti, Alex Jenkins, Ricardo Ferreira, Julie Grollier and Alice Mizrahi
• Abstract summary: We show that magnetic tunnel junctions can process RF inputs with multiple frequencies in parallel. Using a backpropagation-free method called extreme learning, we classify noisy images encoded by RF signals. These results are a key step for embedded radiofrequency artificial intelligence.
• Score: 46.000685134136525
• License: http://creativecommons.org/licenses/by/4.0/
• 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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