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.
@article{arxiv.2403.02946,
title = {SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators},
author = {Mahdi Taheri and Masoud Daneshtalab and Jaan Raik and Maksim Jenihhin and Salvatore Pappalardo and Paul Jimenez and Bastien Deveautour and Alberto Bosio},
journal= {arXiv preprint arXiv:2403.02946},
year = {2024}
}