Demonstrating how a ReRAM-based platform can support on-chip customized learning in real-time at the edge
Abstract:
This paper presents the first experimental demonstration of few-shot on-chip training on an in-memory computing resistive memory (ReRAM) platform. We use the Model-Agnostic Meta-Learning (MAML) algorithm to reduce training iterations and associated ReRAM conductance updates by orders of magnitude. Through co-optimization of device programming conditions and the algorithm, we achieve >97% accuracy on the Omniglot dataset after just five training iterations (i.e., ReRAM programming operations) while improving device retention at 150°C.
Published in: 2025 Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)
Date Added to IEEE Xplore: 18 July 2025