This is the artifact for the paper ``Path Forward Beyond Simulators: Fast and Accurate DNN Execution Time Prediction " to appear in MICRO 2023
This work focuses on the time-predictable execution of Deep Neural Networks (DNNs) accelerated on FP...
Training DNN models for accuracy is resource intensive and needs high-performance computing resource...
Displaying the performance of the ANN for diagnosis of images in the DR dataset for early prediction...
This is the artifact for the paper ``Path Forward Beyond Simulators: Fast and Accurate DNN Execution...
Deep neural network (DNN) latency characterization is a time-consuming process and adds significant ...
This is the artifact of the paper "DSP: Efficient GNN Training with Multiple GPUs"
Designing neural network (NN) to predict time series is not a trivial task. Some kind of science and...
This release contains the initial artifact for the paper MariusGNN: Resource-Efficient Out-of-Core T...
Abstract. We present an estimation methodology, accurately predicting the execution time for a given...
<p>Forecasting performance of the MLP model of ANNs for AIDS in the test set.</p
<p>The computational times for the prediction phases of various ensembles based on three hidden laye...
Current state-of-the-art employs approximate multipliers to address the highly increased power deman...
Deep neural networks (DNNs) are widely used in various artificial intelligence applications and plat...
This is the artifact accompanying the paper "Computing the Expected Execution Time of Probabilistic...
The codes directory includes the various machine learning models, such as FNO, UNet and ResNet. R128...
This work focuses on the time-predictable execution of Deep Neural Networks (DNNs) accelerated on FP...
Training DNN models for accuracy is resource intensive and needs high-performance computing resource...
Displaying the performance of the ANN for diagnosis of images in the DR dataset for early prediction...
This is the artifact for the paper ``Path Forward Beyond Simulators: Fast and Accurate DNN Execution...
Deep neural network (DNN) latency characterization is a time-consuming process and adds significant ...
This is the artifact of the paper "DSP: Efficient GNN Training with Multiple GPUs"
Designing neural network (NN) to predict time series is not a trivial task. Some kind of science and...
This release contains the initial artifact for the paper MariusGNN: Resource-Efficient Out-of-Core T...
Abstract. We present an estimation methodology, accurately predicting the execution time for a given...
<p>Forecasting performance of the MLP model of ANNs for AIDS in the test set.</p
<p>The computational times for the prediction phases of various ensembles based on three hidden laye...
Current state-of-the-art employs approximate multipliers to address the highly increased power deman...
Deep neural networks (DNNs) are widely used in various artificial intelligence applications and plat...
This is the artifact accompanying the paper "Computing the Expected Execution Time of Probabilistic...
The codes directory includes the various machine learning models, such as FNO, UNet and ResNet. R128...
This work focuses on the time-predictable execution of Deep Neural Networks (DNNs) accelerated on FP...
Training DNN models for accuracy is resource intensive and needs high-performance computing resource...
Displaying the performance of the ANN for diagnosis of images in the DR dataset for early prediction...
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