Leveraging a Passive MRAM Crossbar for Hardware-in-the-Loop and Continual learning

Abstract

Artificial neural networks (ANNs) have enabled major advances in artificial intelligence, yet their growing computational and energy demands challenge conventional von Neumann architectures due to the costly separation of memory and processing. In-memory computing has emerged as a promising solution, particularly through memristive crossbar arrays capable of performing multiply-and-accumulate operations directly within memory. Among nonvolatile memory technologies, magnetic tunnel junctions, the core elements of MRAM, offer high endurance, nonvolatility, and CMOS compatibility, making them attractive candidates for neuromorphic systems. In this work, we report inference results on a $15 \times 15$ MRAM crossbar, benchmarking experimental measurements against numerical simulations. We further demonstrate hardware-in-the-loop (HIL) training and experimentally validate metaplasticity mechanisms enabling continual learning on simple tasks. These results highlight the potential of MRAM-based in-memory architectures for adaptive, energy-efficient, and scalable artificial intelligence (AI) hardware.