Tag Archives: Justin

TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications

Posted on August 14, 2020 | 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)
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GPU-TRIDENT: Efficient Modeling of Error Propagation in GPU Programs

Posted on June 30, 2020 | 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)
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Improving the Accuracy of IR-Level Fault Injection

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

Lucas Palazzi, Guanpeng Li, Bo Fang, and Karthik Pattabiraman, To appear in the IEEE Transactions on Dependable and Secure Computing (TDSC). (Acceptance date: March 2020). [PDF] (Code)
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A Tale of Two Injectors: End-to-End Comparison of IR-level and Assembly-Level Fault Injection

Posted on July 21, 2019 | Comments Off on A Tale of Two Injectors: End-to-End Comparison of IR-level and Assembly-Level Fault Injection

Lucas Palazzi, Guanpeng Li, Bo Fang, and Karthik Pattabiraman, IEEE International Symposium on Software Reliability Engineering (ISSRE), 2019. (Acceptance Rate: 31.4%) [ PDF | Talk ] (code)
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BinFI: An Efficient Fault Injector for Safety-Critical Machine Learning Systems

Posted on June 16, 2019 | Comments Off on BinFI: An Efficient Fault Injector for Safety-Critical Machine Learning Systems

Zitao Chen, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2019. (Acceptance Rate: 21%) [ PDF | Talk ] ( Code Finalist for the SC reproducibility challenge (one of 3 papers))
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TensorFI: A Configurable Fault Injector for TensorFlow Applications

Posted on October 15, 2018 | Comments Off on TensorFI: A Configurable Fault Injector for TensorFlow Applications

Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben, Workshop on Software Certification (WoSoCER), 2018, co-located with the IEEE International Symposium on Software Reliability Engineering (ISSRE). 2018. [ PDF | Talk Slides ] (Code)
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Modeling Soft-Error Propagation in Programs

Posted on March 4, 2018 | Comments Off on Modeling Soft-Error Propagation in Programs

Guanpeng Li, Karthik Pattabiraman, Siva Kumar Sastry Hari, Michael Sullivan, and Timothy Tsai. IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2018. (Acceptance Rate for Regular Papers: 25%) [ PDF | Talk ] (Link to Code) (Best Paper Runner up)
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Modeling Input Dependent Error Propagation in Programs

Posted on March 4, 2018 | Comments Off on Modeling Input Dependent Error Propagation in Programs

Guanpeng Li and Karthik Pattabiraman, IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2018. (Acceptance Rate for Regular Papers: 25%) [PDF | Talk] (Link to Code)
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Understanding Error Propagation in Deep-Learning Neural Networks (DNN) Accelerators and Applications

Posted on June 15, 2017 | Comments Off on Understanding Error Propagation in Deep-Learning Neural Networks (DNN) Accelerators and Applications

Guanpeng Li, Siva Hari, Michael Sullivan, Timothy Tsai, Karthik Pattabiraman, Joel Emer, Stephen Keckler, International Conference for High-Performance Computing, Networking, Storage and Analysis (SC), 2017. (Acceptance Rate: 19%) [PDF | Talk] (Injector code)
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Configurable Detection of SDC-Causing Errors in Programs

Posted on November 3, 2016 | Comments Off on Configurable Detection of SDC-Causing Errors in Programs

Qining Lu, Guanpeng Li, Karthik Pattabiraman, Meeta Gupta and Jude Rivers, To appear in the ACM Transactions on Embedded Computing Systems (TECS). [ PDF ]
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