Resistive RAM Endurance Array-Level Characterization and Correction Techniques Targeting Deep Learning Applications

Abstract : 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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https://hal-cea.archives-ouvertes.fr/cea-02186452
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Submitted on : Wednesday, July 17, 2019 - 12:21:38 PM
Last modification on : Friday, September 13, 2019 - 8:38:04 PM

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Alessandro Grossi, Elisa Vianello, Mohamed M. Sabry, Marios Barlas, Laurent Grenouillet, et al.. Resistive RAM Endurance Array-Level Characterization and Correction Techniques Targeting Deep Learning Applications. IEEE Transactions on Electron Devices, Institute of Electrical and Electronics Engineers, 2019, 66 (3), pp.1281-1288. ⟨10.1109/TED.2019.2894387⟩. ⟨cea-02186452⟩

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