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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الحاوية / القاعدة:	IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
المؤلفون الرئيسيون:	Andrea Boni, Francesco Malena, Francesco Saccani, Michele Amoretti, Michele Caselli
التنسيق:	مقال
اللغة:	الإنجليزية
منشور في:	IEEE 2023-01-01
الموضوعات:	Analog in-memory computing convolutional neural networks FD-SOI mixed-signal accelerators resistive random access memory (RRAM)
الوصول للمادة أونلاين:	https://ieeexplore.ieee.org/document/10233848/