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

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...

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Bibliographic Details
Main Authors: Andrea Boni, Francesco Malena, Francesco Saccani, Michele Amoretti, Michele Caselli
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Subjects:
Analog in-memory computing
convolutional neural networks
FD-SOI
mixed-signal accelerators
resistive random access memory (RRAM)
Online Access:https://ieeexplore.ieee.org/document/10233848/

Internet

https://ieeexplore.ieee.org/document/10233848/

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