VCMA-MRAM In-Memory Stochastic Sampling for Edge Boltzmann Machine Inference

Edge intelligence is often limited by the computation–energy trade-off in resource-constrained devices. Boltzmann machines (BMs) provide strong unsupervised learning capability, yet their reliance on Gibbs sampling makes digital implementations costly in both computation and energy. In this paper, we present a voltage-controlled magnetic anisotropy magnetic tunnel junction (VCMA-MTJ)-based MRAM system that performs in-memory stochastic sampling for state generation and updates in restricted/deep Boltzmann machines (RBMs/DBMs). By exploiting the intrinsic stochastic switching of VCMA-MTJs, the proposed system achieves probabilistic sampling with an energy as low as ∼10 fJ per sample. Implemented on a microcontroller-based edge platform, it enables real-time multi-sensor anomaly detection with an F1-score of 0.9854 and stable operation. The proposed hardware–algorithm co-design achieves in situ stochastic computing and storage within a single MRAM cell, providing an ultra-low-power substrate for probabilistic inference at the edge.