SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators-Reference-Cited by-同舟云学术

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

Published:2024-04-03 Issue: Volume: Page:19-24
ISSN:
Container-title:2024 27th International Symposium on Design & Diagnostics of Electronic Circuits & Systems (DDECS)
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Author:

Taheri Mahdi¹,Daneshtalab Masoud²,Raik Jaan¹,Jenihhin Maksim¹,Pappalardo Salvatore³,Jimenez Paul³,Deveautour Bastien³,Bosio Alberto³

Affiliation:

1. Tallinn University of Technology,Tallinn,Estonia

2. Mälardalen University,Västerås,Sweden

3. Ecole Centrale de Lyon,Lyon,France

Funder

Estonian Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10508803/10508898/10508925.pdf?arnumber=10508925

Reference32 articles.

1. Dnn hardware reliability assessment and enhancement;Taheri

2. A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks

3. Emerging Computing Devices: Challenges and Opportunities for Test and Reliability*

4. APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors

5. DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Special Session: Reliability Assessment Recipes for DNN Accelerators;2024 IEEE 42nd VLSI Test Symposium (VTS);2024-04-22

2. Keynote: Cost-Efficient Reliability for Edge-AI Chips;2024 IEEE 25th Latin American Test Symposium (LATS);2024-04-09

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