Single-Event Upset Analysis of a Systolic Array based Deep Neural Network Accelerator
<|reference_start|>Single-Event Upset Analysis of a Systolic Array based Deep Neural Network Accelerator: Deep Neural Network (DNN) accelerators are extensively used to improve the computational efficiency of DNNs, but are prone to faults through Single-Event Upsets (SEUs). In this work, we present an in-depth analysis of the impact of SEUs on a Systolic Array (SA) based DNN accelerator. A fault injection campaign is performed through a Register-Transfer Level (RTL) based simulation environment to improve the observability of each hardware block, including the SA itself as well as the post-processing pipeline. From this analysis, we present the sensitivity, independent of a DNN model architecture, for various flip-flop groups both in terms of fault propagation probability and fault magnitude. This allows us to draw detailed conclusions and determine optimal mitigation strategies.<|reference_end|>
@article{jonckers2024single-event,
    title={Single-Event Upset Analysis of a Systolic Array based Deep Neural
  Network Accelerator},
    author={Na"in Jonckers, Toon Vinck, Gert Dekkers, Peter Karsmakers, Jeffrey
  Prinzie},
    journal={arXiv preprint arXiv:2405.15381},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15381},
    primaryClass={cs.AR eess.SP}
}