期刊
IEEE TRANSACTIONS ON ELECTRON DEVICES
卷 66, 期 3, 页码 1281-1288出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TED.2019.2894387
关键词
Characterization; deep learning; HfO2; performance; reliability; resistive RAM (RRAM); variability
资金
- Defense Advanced Research Projects Agency
- NSF-SRC/Nanoelectronics Research Initiative/Global Research Collaboration Energy-Efficient Computing: From Devices to Architectures
- STARnet SONIC
- NSF
- Stanford SystemX Alliance
- NTU Startup Grant [M4082035]
- AME Programmatic Hardware Software Co-Optimization for Deep Learning [M4070301]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据