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Microwave neural processing and broadcasting with spintronic nano-oscillators
Can we build small neuromorphic chips capable of training deep networks with
billions of parameters? This challenge requires hardware neurons and synapses
with nanometric dimensions, which can be individually tuned, and densely
connected. While nanosynaptic devices have been pursued actively in recent
years, much less has been done on nanoscale artificial neurons. In this paper,
we show that spintronic nano-oscillators are promising to implement analog
hardware neurons that can be densely interconnected through electromagnetic
signals. We show how spintronic oscillators maps the requirements of artificial
neurons. We then show experimentally how an ensemble of four coupled
oscillators can learn to classify all twelve American vowels, realizing the
most complicated tasks performed by nanoscale neurons
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