In-memory computing constitutes a promising route toward more energy-efficient artificial-intelligence hardware as it reduces data transfer between memory and processing units and allows physical devices to directly perform vector-matrix operations. A central challenge, however, is to program the individual non-volatile memory components storing the weights in these dense networks without sacrificing compactness, energy efficiency, or scalability.

Existing approaches face important trade-offs. Passive crossbar arrays require complex biasing schemes across rows and columns, which increase circuit overhead and programming energy. Adding a transistor-based selector restores individual control but increases the cell area by factors up to two orders of magnitude in current demonstrations, while also adding local access routing, and complicating dense 3D integration. In addition, programming and inference typically still rely on distinct peripheral circuits for write driving, sensing and computation. More generally, architectures that rely on individual synapse access preserve programmability, but their wiring overhead makes scaling to dense large networks difficult. This motivates approaches in which many channels can instead be routed through shared physical interconnects.

Among neuromorphic hardware platforms, architectures that exploit frequency multiplexing are especially attractive because many input channels can be routed and processed on shared physical interconnects. This opportunity has been explored for inference in photonic systems based on wavelength-selective microring resonators, microring weight banks and phase-change photonic memories, as well as in spintronic systems including radio-frequency (RF) networks based on magnetic tunnel junctions [1]. Yet in all these platforms, frequency multiplexing applies solely to network inputs for inference, while programming relies on individual electrical access to each synapse, limiting scalability.

Here we introduce a different architecture, in which chains of magnetic tunnel junctions connected in series are addressed through a single shared RF strip line for synaptic programming, state readout and RF input delivery. Unlike individually addressed arrays, this scheme does not require per-synapse selector devices nor dedicated access lines. It exploits the frequency selectivity of magnetic tunnel junctions and the microwave-induced reversal of their magnetic state, here their vortex-core polarity. Because each junction is designed with a distinct resonance frequency, a broadcast RF signal can selectively switch or read any synapse within the chain.

Since all 11 junctions in the experimentally realized chain can be independently programmed through this single shared access, the chain can be placed in any of its 2048 binary configurations. Traversing this configuration space strongly reshapes the spectral transfer function of the chain, which we exploit for RF signal classification.  During inference, RF inputs are frequency multiplexed, so that the input dimensionality is not constrained by the number of magnetic tunnel junctions in the chain. Using two such chains, we physically implement a 22-synapse network that can be remotely reconfigured between handwritten-digit classification and drone RF-signature identification. After reconfiguration for each task, the experimental network reaches 94.91 ± 0.26% and 97.33 ± 0.62% accuracy, respectively, showing that the same hardware can switch between distinct functions while maintaining high task-specific performance. These results identify broadcast RF programming as a scalable route toward compact and rapidly reconfigurable neuromorphic hardware based on spintronic devices [2].