SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators
<|reference_start|>SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators: Systolic array has emerged as a prominent architecture for Deep Neural Network (DNN) hardware accelerators, providing high-throughput and low-latency performance essential for deploying DNNs across diverse applications. However, when used in safety-critical applications, reliability assessment is mandatory to guarantee the correct behavior of DNN accelerators. While fault injection stands out as a well-established practical and robust method for reliability assessment, it is still a very time-consuming process. This paper addresses the time efficiency issue by introducing a novel hierarchical software-based hardware-aware fault injection strategy tailored for systolic array-based DNN accelerators.<|reference_end|>
@article{taheri2024saffira:,
    title={SAFFIRA: a Framework for Assessing the Reliability of
  Systolic-Array-Based DNN Accelerators},
    author={Mahdi Taheri, Masoud Daneshtalab, Jaan Raik, Maksim Jenihhin,
  Salvatore Pappalardo, Paul Jimenez, Bastien Deveautour, and Alberto Bosio},
    journal={arXiv preprint arXiv:2403.02946},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02946},
    primaryClass={cs.AI cs.AR cs.LG}
}