Summary of Saffira: a Framework For Assessing the Reliability Of Systolic-array-based Dnn Accelerators, by Mahdi Taheri et al.
SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators
by Mahdi Taheri, Masoud Daneshtalab, Jaan Raik, Maksim Jenihhin, Salvatore Pappalardo, Paul Jimenez, Bastien Deveautour, Alberto Bosio
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new approach to assessing the reliability of Deep Neural Network (DNN) hardware accelerators, specifically those based on systolic arrays. Systolic arrays are used in various applications due to their high-throughput and low-latency performance. However, ensuring correct behavior in safety-critical scenarios requires reliable assessment. The authors introduce a hierarchical software-based fault injection strategy to improve the time efficiency of reliability testing for these accelerators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The systolic array is an important architecture for DNN hardware accelerators that provides high-throughput and low-latency performance. To guarantee correct behavior in safety-critical applications, it’s essential to assess the reliability of these accelerators. The paper presents a novel approach that uses software-based fault injection to improve the time efficiency of reliability testing. |
Keywords
* Artificial intelligence * Neural network
