Special Session: On the Reliability of Conventional and Quantum Neural Network Hardware

Mehdi Sadi, Yi He, Yanjing Li, Mahabubul Alam, Satwik Kundu, Swaroop Ghosh, Javad Bahrami, Naghmeh Karimi

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


Neural Networks (NNs) are being extensively used in critical applications such as aerospace, healthcare, autonomous driving, and military, to name a few. Limited precision of the underlying hardware platforms, permanent and transient faults injected unintentionally as well as maliciously, and voltage/temperature fluctuations can potentially result in malfunctions in NNs with consequences ranging from substantial reduction in the network accuracy to jeopardizing the correct prediction of the network in worst cases. To alleviate such reliability concerns, this paper discusses the state-of-the-art reliability enhancement schemes that can be tailored for deep learning accelerators. We will discuss the errors associated with the hardware implementation of Deep-Learning (DL) algorithms along with their corresponding countermeasures. An in-field self-test methodology with a high test coverage is introduced, and an accurate high-level framework, so-called FIdelity, is proposed that enables the designers to evaluate DL accelerators in presence of such errors. Then, a state-of-the-art robustness-preserving training algorithm based on the Hessian Regularization is introduced. This algorithm alleviates the perturbations during inference time with negligible degradation in the accuracy of the network. Finally, Quantum Neural Networks (QNNs) and the methods to make them resilient against a variety of vulnerabilities such as fault injection, spatial and temporal variations in Qubits, and noise in QNNs are discussed.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 40th VLSI Test Symposium, VTS 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781665410601
StatePublished - 2022
Event40th IEEE VLSI Test Symposium, VTS 2022 - Virtual, Online, United States
Duration: Apr 25 2022Apr 27 2022

Publication series

NameProceedings of the IEEE VLSI Test Symposium


Conference40th IEEE VLSI Test Symposium, VTS 2022
Country/TerritoryUnited States
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


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