SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators

Mahdi Taheri; Masoud Daneshtalab; Jaan Raik; Maksim Jenihhin; Salvatore Pappalardo; Paul Jimenez; Bastien Deveautour; Alberto Bosio

arXiv · 2403.02946

SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators

Mahdi Taheri·Masoud Daneshtalab·Jaan Raik·Maksim Jenihhin·Salvatore Pappalardo·Paul Jimenez·Bastien Deveautour·Alberto Bosio

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Abstract

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.

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Mahdi Taheri, Masoud Daneshtalab, Jaan Raik, Maksim Jenihhin, Salvatore Pappalardo, Paul Jimenez, Bastien Deveautour, Alberto Bosio. 2024-03-05. SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators. https://arxiv.org/abs/2403.02946

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