Add to ORKGDynamic graph neural network (DGNN) is becoming increasingly popular because of its widespread use in capturing dynamic features in the real world. A variety of dynamic graph neural networks designed from algorithmic perspectives have succeeded in incorporating temporal information into graph processing. Despite the promising algorithmic performance, deploying DGNNs on hardware presents additional challenges due to the model complexity, diversity, and the nature of the time dependency. Meanwhile, the differences between DGNNs and static graph neural networks make hardware-related optimizations for static graph neural networks unsuitable for DGNNs. In this paper, we select eight prevailing DGNNs with different characteristics and profile the...
Graph neural networks (GNNs) are rapidly becoming the dominant way to learn on graph-structured data...
Deep neural network (DNN) inference is increasingly being executed on mobile and embedded platforms ...
The ability to accurately predict deep neural network (DNN) inference performance metrics, such as l...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
Deep Neural Network (DNN) inference is increasingly being deployed on edge devices, driven by the ad...
A graph is a ubiquitous data structure that models entities and their interactions through the colle...
Machine learning on graph data has gained significant interest because of its applicability to vario...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
This repository contains the code for Dynamic Graph Neural Networks on Hardware: Bottleneck Analysis...
Graph neural networks (GNNs) have received great attention due to their success in various graph-rel...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
Deep learning has been widely adopted for different applications of artificial intelligence-speech r...
As the interest to Graph Neural Networks (GNNs) is growing, the importance of benchmarking and perfo...
Deep learning frameworks optimize the computation graphs and intra-operator computations to boost th...
Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicabilit...
Graph neural networks (GNNs) are rapidly becoming the dominant way to learn on graph-structured data...
Deep neural network (DNN) inference is increasingly being executed on mobile and embedded platforms ...
The ability to accurately predict deep neural network (DNN) inference performance metrics, such as l...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
Deep Neural Network (DNN) inference is increasingly being deployed on edge devices, driven by the ad...
A graph is a ubiquitous data structure that models entities and their interactions through the colle...
Machine learning on graph data has gained significant interest because of its applicability to vario...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
This repository contains the code for Dynamic Graph Neural Networks on Hardware: Bottleneck Analysis...
Graph neural networks (GNNs) have received great attention due to their success in various graph-rel...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
Deep learning has been widely adopted for different applications of artificial intelligence-speech r...
As the interest to Graph Neural Networks (GNNs) is growing, the importance of benchmarking and perfo...
Deep learning frameworks optimize the computation graphs and intra-operator computations to boost th...
Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicabilit...
Graph neural networks (GNNs) are rapidly becoming the dominant way to learn on graph-structured data...
Deep neural network (DNN) inference is increasingly being executed on mobile and embedded platforms ...
The ability to accurately predict deep neural network (DNN) inference performance metrics, such as l...