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On the Reliability of Computing-in-Memory Accelerators for Deep Neural Networks

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System Dependability and Analytics

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

Abstract

Computing-in-memory with emerging non-volatile memory (nvCiM) is shown to be a promising candidate for accelerating deep neural networks (DNNs) with high energy efficiency. However, most non-volatile memory (NVM) devices suffer from reliability issues, resulting in a difference between actual data involved in the nvCiM computation and the weight value trained in the data center. Thus, models actually deployed on nvCiM platforms achieve lower accuracy than their counterparts trained on the conventional hardware (e.g., GPUs). In this chapter, we first offer a brief introduction to the opportunities and challenges of nvCiM DNN accelerators and then show the properties of different types of NVM devices. We then introduce the general architecture of nvCiM DNN accelerators. After that, we discuss the source of unreliability and how to efficiently model their impact. Finally, we introduce representative works that mitigate the impact of device variations.

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Notes

  1. 1.

    Note that each element of the output are deeply co-related, not independent.

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Authors and Affiliations

  1. University of Notre Dame, Notre Dame, IN, USA

    Zheyu Yan, Xiaobo Sharon Hu & Yiyu Shi

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  1. Zheyu Yan
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  2. Xiaobo Sharon Hu
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  3. Yiyu Shi
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Corresponding author

Correspondence to Yiyu Shi .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Long Wang

  2. University of British Columbia, Vancouver, BC, Canada

    Karthik Pattabiraman

  3. Nokia Bell Labs, São Paulo, Brazil

    Catello Di Martino

  4. Mayo Clinic, Rochester, MN, USA

    Arjun Athreya

  5. Purdue University, West Lafayette, IN, USA

    Saurabh Bagchi

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Yan, Z., Hu, X.S., Shi, Y. (2023). On the Reliability of Computing-in-Memory Accelerators for Deep Neural Networks. In: Wang, L., Pattabiraman, K., Di Martino, C., Athreya, A., Bagchi, S. (eds) System Dependability and Analytics. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-02063-6_9

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  • DOI: https://doi.org/10.1007/978-3-031-02063-6_9

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