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Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing
May 9, 2024, 4:42 a.m. | Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, Roland Renner, \v{S}pela Brglez, Thomas Limbacher, Enrique Pi\~nero, Alejandro Linares Barra
cs.LG updates on arXiv.org arxiv.org
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, computational resources, and data. In contrast, humans can effortlessly adjust to new tasks by transferring knowledge from related ones. The concept of learning-to-learn (L2L) mimics this process and enables AI models to rapidly …
abstract adapt ai models artificial artificial intelligence arxiv change computational computing cs.lg cs.ne current data demand deployment edge fine-tuning however in-memory in-memory computing intelligence learn low memory phase-change memory power resources struggle systems the edge type
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