Tag Archives: reliability

ImmunoPlane: Middleware for Providing Adaptivity to Distributed Internet-of-Things Applications

Posted on January 19, 2024 by | Comments Off on ImmunoPlane: Middleware for Providing Adaptivity to Distributed Internet-of-Things Applications

Kumseok Jung, Gargi Mitra, Sathish Gopalakrishnan and Karthik Pattabiraman, To appear in the Proceedings of the ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI), 2024. (Acceptance Rate: TBD) [ PDF | Talk] (code)
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Characterizing and Improving Resilience of Accelerators to Memory Errors in Autonomous Robots

Posted on September 24, 2023 by | Comments Off on Characterizing and Improving Resilience of Accelerators to Memory Errors in Autonomous Robots

Deval Shah, Zi Yu Xue, Karthik Pattabiraman and Tor Aamodt, To appear in the ACM Transactions on Cyber-Physical Systems (TCPS). (Acceptance date: Sep 2023). [PDF] (arXIV)
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Evaluating the Effect of Common Annotation Faults on Object Detection Techniques

Posted on August 7, 2023 by | Comments Off on Evaluating the Effect of Common Annotation Faults on Object Detection Techniques

Abraham Chan, Arpan Gujarati, Karthik Pattabiraman and Sathish Gopalakrishnan, Proceedings of the IEEE International Symposium on Software Reliability Engineering (ISSRE), 2023. (Acceptance Rate: 28.5%) [ PDF | Talk ] (Code). Artifacts Available and Reviewed.

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Resilience Assessment of Large Language Models under Transient Hardware Faults

Posted on August 7, 2023 by | Comments Off on Resilience Assessment of Large Language Models under Transient Hardware Faults

Udit Agarwal, Abraham Chan, and Karthik Pattabiraman, Proceedings of the IEEE International Symposium on Software Reliability Engineering (ISSRE), 2023. (Acceptance Rate: 28.5%) [ PDF | Talk ] (Code). Artifacts Available and Reviewed.
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Mixed Precision Support in HPC Applications: What About Reliability?

Posted on July 20, 2023 by | Comments Off on Mixed Precision Support in HPC Applications: What About Reliability?

Alessio Netti, Yang Peng, Patrik Omland, Michael Paulitsch, Jorge Parra, Gustavo Espinosa, Udit Agarwal, Abraham Chan, and Karthik Pattabiraman, Journal of Parallel and Distributed Computing (JPDC). [ PDF ] (code)
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Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults

Posted on June 16, 2023 by | Comments Off on Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults

Ali Asgari, Florian Geissler, Syed Qutub, Michael Paulitsch, Prashant Nair, and Karthik Pattabiraman, Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2023. (Acceptance Rate: 23.9%) [ PDF | Talk ] (code). Artifacts Available and Functional
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A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks

Posted on April 22, 2023 by | Comments Off on A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks

Florian Geissler, Syed Qutub, Michael Paulitsch and Karthik Pattabiraman, Proceedings of the International Conference on Computer Safety, Reliability and Security (SafeComp), 2023. (Acceptance Rate: 20%) [PDF | Talk]
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LLTFI: Framework Agnostic Fault Injection for Machine Learning Applications (Tools and Artifact Track)

Posted on July 29, 2022 by | Comments Off on LLTFI: Framework Agnostic Fault Injection for Machine Learning Applications (Tools and Artifact Track)

Udit Agarwal, Abraham Chan, and Karthik Pattabiraman, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2022. (Acceptance Rate: 29%) [ PDF | Talk (video) ] (Code)
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Fault Injection for TensorFlow Applications

Posted on May 10, 2022 by | Comments Off on Fault Injection for TensorFlow Applications

Niranjhana Narayanan, Zitao Chen, Bo Fang, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben, IEEE Transactions on Dependable and Secure Computing (TDSC). Acceptance Date: May 2022. [ PDF ] (code1, code2)
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The Fault in Our Data Stars: Studying Mitigation Techniques against Faulty Training Data in ML Applications

Posted on March 14, 2022 by | Comments Off on The Fault in Our Data Stars: Studying Mitigation Techniques against Faulty Training Data in ML Applications

Abraham Chan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan. IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2022. (Acceptance rate: 18.7%) [ PDF | Talk ] (Code)
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