학술논문

Resistive RAM Endurance: Array-Level Characterization and Correction Techniques Targeting Deep Learning Applications.
Document Type
Article
Source
IEEE Transactions on Electron Devices. Mar2019, Vol. 66 Issue 3, p1281-1288. 8p.
Subject
*ELECTRIC breakdown
*DEEP learning
*NONVOLATILE random-access memory
*HAFNIUM compounds
*ELECTRIC field effects
Language
ISSN
0018-9383
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. [ABSTRACT FROM AUTHOR]