Tag Archives: Resilient

Understanding the Resilience of Neural Network Ensembles against Faulty Training Data

Posted on October 22, 2021 by | Comments Off on Understanding the Resilience of Neural Network Ensembles against Faulty Training Data

Abraham Chan, Niranjhana Narayananan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan, IEEE International Symposium on Quality, Reliability and Security (QRS), 2021. Full paper (Acceptance Rate: 25.1%) [ PDF | Talk | Video ] Best Paper Award (1 of 3)

Continue reading →

Comments Off on Understanding the Resilience of Neural Network Ensembles against Faulty Training Data

Posted in papers

Tagged , , , , , , , ,

Towards a safety case for hardware-fault tolerance in convolutional neural networks using activation range supervision

Posted on August 21, 2021 by | Comments Off on Towards a safety case for hardware-fault tolerance in convolutional neural networks using activation range supervision

Florian Geissler, Syed Qutub, Sayanta Roychowdhury, Ali Asgari, Yang Peng, Akash Dhamasia, Ralf Graefe, Karthik Pattabiraman and Michael Paulitsch, AI Safety Workshop 2021, Best Paper Award Nominee (1 of 4) [ PDF | Talk ] (arXIV)
Continue reading →

Comments Off on Towards a safety case for hardware-fault tolerance in convolutional neural networks using activation range supervision

Posted in papers

Tagged , , , , ,

A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction

Posted on March 10, 2021 by | Comments Off on A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction

Zitao Chen, Guanpeng Li, and Karthik Pattabiraman, IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2021. (Acceptance Rate: 16.5%). [ PDF | Talk , Video] (arXIV, code) Best Paper Award Runner up (1 of 2 among nearly 300 submissions). Incorporated into Intel’s OpenVino2 Framework (More details, Documentation).
Continue reading →

Comments Off on A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction

Posted in papers

Tagged , , , , , , , ,

An Empirical Study of the Impact of Single and Multiple Bit-Flip Errors in Programs

Posted on October 20, 2020 by | Comments Off on An Empirical Study of the Impact of Single and Multiple Bit-Flip Errors in Programs

Behrooz Sangchoolie, Karthik Pattabiraman and Johan Karlsson, IEEE Transactions on Dependable and Secure Computing (TDSC). [ PDF ]
Continue reading →

Comments Off on An Empirical Study of the Impact of Single and Multiple Bit-Flip Errors in Programs

Posted in papers

Tagged , , , , ,

New Wine in an Old Bottle: N-Version Programming for Machine Learning Components

Posted on September 1, 2020 by | Comments Off on New Wine in an Old Bottle: N-Version Programming for Machine Learning Components

Arpan Gujarati, Sathish Gopalakrishnan, and Karthik Pattabiraman, IEEE International Workshop on Software Certification (WoSoCER), 2020. Held in conjunction with the IEEE International Symposium on Software Reliability Engineering (ISSRE), 2020. [PDF][Talk]
Continue reading →

Comments Off on New Wine in an Old Bottle: N-Version Programming for Machine Learning Components

Posted in papers

Tagged , , , , ,

How Far Have We Come in Detecting Anomalies in Distributed Systems? An Empirical Study with a Statement-level Fault Injection Method

Posted on August 14, 2020 by | Comments Off on How Far Have We Come in Detecting Anomalies in Distributed Systems? An Empirical Study with a Statement-level Fault Injection Method

Yong Yang, Yifan Yu, Karthik Pattabiraman, Long Wang, Ying Li, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2020. (Acceptance Rate: 26%). [ PDF | Talk ] (Code)
Continue reading →

Comments Off on How Far Have We Come in Detecting Anomalies in Distributed Systems? An Empirical Study with a Statement-level Fault Injection Method

Posted in papers

Tagged , , , , ,

TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications

Posted on August 14, 2020 by | Comments Off on TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications

Zitao Chen, Niranjhana Narayanan, Bo Fang, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2020. (Acceptance Rate: 26%) [ PDF | Talk ] (Code)
Continue reading →

Comments Off on TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications

Posted in papers

Tagged , , , , , , , ,

GPU-TRIDENT: Efficient Modeling of Error Propagation in GPU Programs

Posted on June 30, 2020 by | Comments Off on GPU-TRIDENT: Efficient Modeling of Error Propagation in GPU Programs

Abdul Rehman Anwer, Guanpeng Li, Karthik Pattabiraman, Michael Sullivan, Timothy Tsai and Siva Hari, ACM International Conference on High-Performance Computing, Networking, Storage, and Analyzis (SC), 2020 (Acceptance Rate: 25.1%) [PDF | Talk] (Code)
Continue reading →

Comments Off on GPU-TRIDENT: Efficient Modeling of Error Propagation in GPU Programs

Posted in papers

Tagged , , , , , ,

TraceSanitizer – Eliminating the Effects of Non-determinism on Error Propagation Analysis

Posted on April 26, 2020 by | Comments Off on TraceSanitizer – Eliminating the Effects of Non-determinism on Error Propagation Analysis

Habib Saissi, Stefan Winter, Oliver Schwan, Karthik Pattabiraman, and Neeraj Suri, IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2020. (Acceptance Rate: 16.5%). [PDF | Talk] (Code)
Continue reading →

Comments Off on TraceSanitizer – Eliminating the Effects of Non-determinism on Error Propagation Analysis

Posted in papers

Tagged , , , ,

Improving the Accuracy of IR-Level Fault Injection

Posted on April 4, 2020 by | Comments Off on Improving the Accuracy of IR-Level Fault Injection

Lucas Palazzi, Guanpeng Li, Bo Fang, and Karthik Pattabiraman, IEEE Transactions on Dependable and Secure Computing (TDSC). (Acceptance date: March 2020). [PDF] (Code)
Continue reading →

Comments Off on Improving the Accuracy of IR-Level Fault Injection

Posted in papers

Tagged , , , , , ,