8 citations · 24 across the 7 of their papers we have counts for
8 papers
Accelerating Graph Neural Networks with a Novel Matrix Compression Format
João N. F. Alves, Samir Moustafa, Siegfried Benkner +3
The inference and training stages of Graph Neural Networks (GNNs) are often dominated by the time required to compute a long sequence of matrix multiplications between the sparse g…
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
Lorenz Kummer, Samir Moustafa, Nils N. Kriege +1
Prior attacks on graph neural networks have mostly focused on graph poisoning and evasion, neglecting the network's weights and biases. Traditional weight-based fault injection att…
On the Two Sides of Redundancy in Graph Neural Networks
Franka Bause, Samir Moustafa, Johannes Langguth +2
Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes. With increasing depth, information from more distant nodes i…
Adaptive Precision Training (AdaPT): A dynamic fixed point quantized training approach for DNNs
Lorenz Kummer, Kevin Sidak, Tabea Reichmann +1
Quantization is a technique for reducing deep neural networks (DNNs) training and inference times, which is crucial for training in resource constrained environments or application…
Resiliency in Numerical Algorithm Design for Extreme Scale Simulations
Emmanuel Agullo, Mirco Altenbernd, Hartwig Anzt +33
This work is based on the seminar titled ``Resiliency in Numerical Algorithm Design for Extreme Scale Simulations'' held March 1-6, 2020 at Schloss Dagstuhl, that was attended by a…
Algorithm-Based Checkpoint-Recovery for the Conjugate Gradient Method
Carlos Pachajoa, Christina Pacher, Markus Levonyak +1
As computers reach exascale and beyond, the incidence of faults will increase. Solutions to this problem are an active research topic. We focus on strategies to make the preconditi…