Resistive RAM endurance : array-level characterization and correction techniques targeting deep learning applications

Resistive RAM endurance : array-level characterization and correction techniques targeting deep learning applications

Limited endurance of resistive RAM (RRAM) is a major challenge for future computing systems. Using thorough endurance tests that incorporate fine-grained read operations at the array level, we quantify for the first time temporary write failures (TWFs) caused by intrinsic RRAM cycle-to-cycle and cel...

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Bibliographic Details
Main Authors: Grossi, Alessandro, Vianello, Elisa, Mohamed M. Sabry, Barlas, Marios, Grenouillet, Laurent, Coignus, Jean, Beigne, Edith, Wu, Tony, Le, Binh Q, Wootters, Mary K., Zambelli, Cristian, Nowak, Etienne, Mitra, Subhasish
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2020
Subjects:
Engineering::Computer science and engineering
Characterization
Deep Learning
Online Access:https://hdl.handle.net/10356/143255
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Institution: Nanyang Technological University
Language: English
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Summary:Limited endurance of resistive RAM (RRAM) is a major challenge for future computing systems. Using thorough endurance tests that incorporate fine-grained read operations at the array level, we quantify for the first time temporary write failures (TWFs) caused by intrinsic RRAM cycle-to-cycle and cell-to-cell variations. We also quantify permanent write failures (PWFs) caused by irreversible breakdown/dissolution of the conductive filament. We show how technology-, RRAM programing-, and system resilience-level solutions can be effectively combined to design new generations of energy-efficient computing systems that can successfully run deep learning (and other machine learning) applications despite TWFs and PWFs. We analyze corresponding system lifetimes and TWF bit error ratio.

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