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

• URL: http://arxiv.org/abs/2403.02946v1
• Date: Tue, 5 Mar 2024 13:17:09 GMT
• Title: SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators
• Authors: Mahdi Taheri, Masoud Daneshtalab, Jaan Raik, Maksim Jenihhin, Salvatore Pappalardo, Paul Jimenez, Bastien Deveautour, and Alberto Bosio
• Abstract summary: This paper introduces a novel hierarchical software-based hardware-aware fault injection strategy tailored for systolic array-based Deep Neural Network (DNN) accelerators.
• Score: 0.4391603054571586
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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