Cost- And Dataset-free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators

Giju Jung, Mohammed Fouda, Sugil Lee, Jongeun Lee, Ahmed Eltawil, Fadi Kurdahi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Resistive RAMs can implement extremely efficient matrix vector multiplication, drawing much attention for deep learning accelerator research. However, high fault rate is one of the fundamental challenges of ReRAM crossbar array-based deep learning accelerators. In this paper we propose a dataset-free, cost-free method to mitigate the impact of stuck-at faults in ReRAM crossbar arrays for deep learning applications. Our technique exploits the statistical properties of deep learning applications, hence complementary to previous hardware or algorithmic methods. Our experimental results using MNIST and CIFAR-10 datasets in binary networks demonstrate that our technique is very effective, both alone and together with previous methods, up to 20 % fault rate, which is higher than the previous remapping methods. We also evaluate our method in the presence of other non-idealities such as variability and IR drop.
Original languageEnglish (US)
Title of host publication2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1733-1738
Number of pages6
ISBN (Print)9783981926354
DOIs
StatePublished - Feb 1 2021

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