This is the extended artifact repo for paper Understanding and Mitigating Hardware Failures in Deep Learning Training Accelerator Systems
This is the artifact of the paper "DSP: Efficient GNN Training with Multiple GPUs"
Overview of Key Challenges and Potential Resolutions in the Utilization of Machine Learning or Deep ...
This package is the demo of our paper, whose title is Understanding Performance Problems in Deep Lea...
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, a...
Research Artifact for USENIX Security 2023 paper "Decompiling x86 Deep Neural Network Executables
This is the artifact for our OOPSLA'23 paper “Run-Time Prevention of Software Integration Failures o...
Deep neural networks have achieved phenomenal successes in vision recognition tasks, which motivate ...
This is the artifact of the PLDI 2023 article "Architecture-Preserving Provable Repair of Deep Neura...
Artifacts for the "Resilience Assessment of Large Language Models under Transient Hardware Faults" P...
Remarkable hardware robustness of deep learning (DL) is revealed by error injection analyses perform...
2018-02-02This thesis is dedicated to improving the efficiency of resilient computing through both a...
This is the code and main datasets for artifact 'DRFuzz: A Regression Fuzzing Framework for Deep Lea...
This document is the artifact description of the paper entitled “Apollo: Automatic Partition-based O...
In recent years, the big data booming has boosted the development of highly accurate prediction mode...
The use of Neural Network (NN) inference on edge devices necessitates the development of customized ...
This is the artifact of the paper "DSP: Efficient GNN Training with Multiple GPUs"
Overview of Key Challenges and Potential Resolutions in the Utilization of Machine Learning or Deep ...
This package is the demo of our paper, whose title is Understanding Performance Problems in Deep Lea...
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, a...
Research Artifact for USENIX Security 2023 paper "Decompiling x86 Deep Neural Network Executables
This is the artifact for our OOPSLA'23 paper “Run-Time Prevention of Software Integration Failures o...
Deep neural networks have achieved phenomenal successes in vision recognition tasks, which motivate ...
This is the artifact of the PLDI 2023 article "Architecture-Preserving Provable Repair of Deep Neura...
Artifacts for the "Resilience Assessment of Large Language Models under Transient Hardware Faults" P...
Remarkable hardware robustness of deep learning (DL) is revealed by error injection analyses perform...
2018-02-02This thesis is dedicated to improving the efficiency of resilient computing through both a...
This is the code and main datasets for artifact 'DRFuzz: A Regression Fuzzing Framework for Deep Lea...
This document is the artifact description of the paper entitled “Apollo: Automatic Partition-based O...
In recent years, the big data booming has boosted the development of highly accurate prediction mode...
The use of Neural Network (NN) inference on edge devices necessitates the development of customized ...
This is the artifact of the paper "DSP: Efficient GNN Training with Multiple GPUs"
Overview of Key Challenges and Potential Resolutions in the Utilization of Machine Learning or Deep ...
This package is the demo of our paper, whose title is Understanding Performance Problems in Deep Lea...
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