Update Disturbance‐Resilient Analog ReRAM Crossbar Arrays for In‐Memory Deep Learning Accelerators

Abstract Resistive memory (ReRAM) technologies with crossbar array architectures hold significant potential for analog AI accelerator hardware, enabling both in‐memory inference and training. Recent developments have successfully demonstrated inference acceleration by offloading compute‐heavy traini...

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Bibliografische Detailangaben

Hauptverfasser:	Wooseok Choi, Tommaso Stecconi, Donato Francesco Falcone, Matteo Galetta, Victoria Clerico, Elisa Zaccaria, Mamidala Saketh Ram, Antonio La Porta, Folkert Horst, Daniel Jubin, Matias Senger, Marilyne Sousa, Steffen Reidt, Ralph Heller, Bernabe Linares‐Barranco, Valeria Bragaglia, Bert Jan Offrein
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Wiley 2026-01-01
Schriftenreihe:	Advanced Science
Schlagworte:	analog in‐memory computing crossbar array deep learning accelerator parallel weight update ReRAM
Online-Zugang:	https://doi.org/10.1002/advs.202504578
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