Boosting RRAM-Based Mixed-Signal Accelerators in FD-SOI Technology for ML Applications

This article presents the flipped (F)-2T2R resistive random access memory (RRAM) compute cell enhancing the performance of RRAM-based mixed-signal accelerators for deep neural networks (DNNs) in machine-learning (ML) applications. The F-2T2R cell is designed to exploit the features of the FD-SOI tec...

Descrizione completa

Dettagli Bibliografici

Autori principali:	Andrea Boni, Francesco Malena, Francesco Saccani, Michele Amoretti, Michele Caselli
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2023-01-01
Serie:	IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Soggetti:	Analog in-memory computing convolutional neural networks FD-SOI mixed-signal accelerators resistive random access memory (RRAM)
Accesso online:	https://ieeexplore.ieee.org/document/10233848/
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!