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DTSTAMP:20211208T164413Z
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DTSTART;TZID=America/Los_Angeles:20211209T141600
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UID:dac_DAC 2021_sess118_RESEARCH701@linklings.com
SUMMARY:Low-Cost and Effective Fault-Tolerance Enhancement Techniques for 
 Emerging Memories-Based Deep Neural Networks
DESCRIPTION:Research Manuscript\n\nLow-Cost and Effective Fault-Tolerance 
 Enhancement Techniques for Emerging Memories-Based Deep Neural Networks\n\
 nNguyen\n\nEmerging non-volatile memory (NVM) technologies with better sca
 lability, non-volatility, and good read performance are widely researched,
  especially for the Deep Neural Networks (DNNs) applications. However, eme
 rging NVMs face reliability concerns due to stuck-at faults, which can sev
 erely degrade the accuracy of DNNs. This paper introduces lightweight and 
 simple yet effective intra-block address remapping and weight encoding tec
 hniques to enhance the fault-tolerance of DNNs. Experiments using popular 
 DNNs indicate that the proposed techniques can enhance the fault-tolerance
  of DNNs by up to 300x on Cifar10 dataset and up to 125x on Imagenet datas
 et.\n\nTopic: Design\n\nKeyword: AI/ML System Design\n\nVirtual Program: P
 resented In-Person
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