Remotely programming the weights of a spintronic neural network by a radiofrequency broadcast signal

TL;DR

This paper demonstrates a scalable method for remotely programming spintronic neural network weights using broadcast radiofrequency signals, enabling rapid reconfiguration for different tasks without individual access lines.

Contribution

It introduces a frequency-selective, remote programming technique for spintronic synaptic weights, eliminating the need for individual access lines and enabling versatile neuromorphic hardware reconfiguration.

Findings

Successfully programmed 11 vortex-based magnetic tunnel junctions remotely.

Reconfigured a 22-synapse network for digit classification and RF-signature identification.

Achieved high accuracy in digit recognition (94.91%) and drone RF identification (97.33%).

Abstract

Selectively programming large number of non-volatile synaptic weights without compromising scalability is a key challenge for in-memory computing. Here, we 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. The programming relies on frequency-selective reversal of the vortex-core polarity and therefore does not require individual access lines or selector devices. By reconfiguring the binary states of these chains, we reshape the weighted sums they perform on frequency-multiplexed RF inputs. Using a 22-synapse network composed of two such chains, we remotely reconfigure the same hardware to perform two distinct tasks: handwritten-digit classification and drone RF-signature identification. The digit-optimized configuration reaches 94.91 +/- 0.26% accuracy on handwritten digits but only 13.17 +/- 0.47% on drone RF signatures, whereas the drone-optimized configuration reaches 97.33 +/- 0.62% on drones but only 47.59 +/- 1.5% on digits. Broadcast RF programming thus provides a compact and scalable route to rapidly reconfigurable spintronic neuromorphic hardware.