Remotely programming the weights of a spintronic neural network by a radiofrequency broadcast signal
M. Menshawy, D. Sanz-Hernández, L. Mazza, V. Puliafito, G. Finocchio, A. Jenkins, R. Ferreira, L. Benetti 等 10 位作者
Selectively programming a large number of non-volatile synaptic weights without compromising scalability is a key challenge for in-memory computing. Here, we experimentally demonstrate remote programming of synaptic weights in series-connected chains of 11 vortex-based magnetic tunnel junctions using broadcast radiofrequency signals applied through a shared strip line. Because each junction is engineered with a distinct resonance frequency, programming relies on frequency-selective reversal of the vortex-core polarity and therefore does not require individual access lines or selector devices. Reconfiguring the binary states of these chains strongly reshapes the continuous spectral transfer functions that determine the weighted sums performed on frequency-multiplexed RF inputs. This allows compact networks built from such chains to process high-dimensional RF-encoded inputs. We experimentally perform handwritten-digit classification and drone RF-signature identification using a two-chain network comprising only 22 synapses in total. After remote reconfiguration for each task, the spintronic network respectively reaches 93.9 +/- 2.3 % and 99.5 +/- 1 % test accuracy, on par with the software baseline. Broadcast RF programming thus provides a compact and scalable route to rapidly reconfigurable spintronic neuromorphic hardware.
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