SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916147348111360 |
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| author | Taheri, Mahdi Daneshtalab, Masoud Raik, Jaan Jenihhin, Maksim Pappalardo, Salvatore Jimenez, Paul Deveautour, Bastien Bosio, Alberto |
| author_facet | Taheri, Mahdi Daneshtalab, Masoud Raik, Jaan Jenihhin, Maksim Pappalardo, Salvatore Jimenez, Paul Deveautour, Bastien Bosio, Alberto |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02946 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators Taheri, Mahdi Daneshtalab, Masoud Raik, Jaan Jenihhin, Maksim Pappalardo, Salvatore Jimenez, Paul Deveautour, Bastien Bosio, Alberto Artificial Intelligence Hardware Architecture Machine Learning 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. |
| title | SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators |
| topic | Artificial Intelligence Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2403.02946 |