Rapid learning with phase-change memory-based in-memory computing through learning-to-learn

Rapid learning with phase-change memory-based in-memory computing through learning-to-learn

Abstract There is a growing demand for low-power, autonomously learning artificial intelligence (AI) systems that can be applied at the edge and rapidly adapt to the specific situation at deployment site. However, current AI models struggle in such scenarios, often requiring extensive fine-tuning, c...

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Main Authors: Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, Roland Renner, Špela Brglez, Thomas Limbacher, Enrique Piñero, Alejandro Linares-Barranco, Angeliki Pantazi, Robert Legenstein
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-56345-4
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https://doi.org/10.1038/s41467-025-56345-4

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