Add to ORKGAbstract—Many real world computer vision applications are required to run on hardware with limited computing power, often referred to as ”edge devices”. The state of the art in computer vision continues towards ever bigger and deeper neural networks with equally rising computational requirements. Model compression methods promise to substantially reduce the computation time and memory demands with little to no impact on the model robustness. However, evaluation of the compression is mostly based on theoretic speedups in terms of required floating-point operations. This work offers a tool to profile the actual speedup offered by several compression algorithms. Our results show a significant discrepancy between the theoretical and actual spee...
Deep neural networks have demonstrated outstanding performance in various fields of machine learning...
Parallel hardware accelerators, for example Graphics Processor Units, have limited on-chip memory ca...
In the wake of the success of convolutional neural networks in image classification, object recognit...
peer reviewedAbstract—Many real world computer vision applications are required to run on hardware w...
The recent advances in deep neural networks (DNNs) make them attractive for embedded systems. Howeve...
The recent advances in deep neural networks (DNNs) make them attractive for embedded systems. Howeve...
In recent years, Deep Neural Networks (DNNs) have become an area of high interest due to it's ground...
Deep neural networks (DNNs) are a key technology nowadays and the main driving factor for many recen...
Deep learning has been found to be an effective solution to many problems in the field of computer ...
After the tremendous success of convolutional neural networks in image classification, object detect...
Parallel hardware accelerators, for example Graphics Processor Units, have limited on-chip memory ca...
After the tremendous success of convolutional neural networks in image classification, object detect...
In the wake of the success of convolutional neural networks in image classification, object recognit...
In the wake of the success of convolutional neural networks in image classification, object recognit...
Deep neural networks have demonstrated outstanding performance in various fields of machine learning...
Deep neural networks have demonstrated outstanding performance in various fields of machine learning...
Parallel hardware accelerators, for example Graphics Processor Units, have limited on-chip memory ca...
In the wake of the success of convolutional neural networks in image classification, object recognit...
peer reviewedAbstract—Many real world computer vision applications are required to run on hardware w...
The recent advances in deep neural networks (DNNs) make them attractive for embedded systems. Howeve...
The recent advances in deep neural networks (DNNs) make them attractive for embedded systems. Howeve...
In recent years, Deep Neural Networks (DNNs) have become an area of high interest due to it's ground...
Deep neural networks (DNNs) are a key technology nowadays and the main driving factor for many recen...
Deep learning has been found to be an effective solution to many problems in the field of computer ...
After the tremendous success of convolutional neural networks in image classification, object detect...
Parallel hardware accelerators, for example Graphics Processor Units, have limited on-chip memory ca...
After the tremendous success of convolutional neural networks in image classification, object detect...
In the wake of the success of convolutional neural networks in image classification, object recognit...
In the wake of the success of convolutional neural networks in image classification, object recognit...
Deep neural networks have demonstrated outstanding performance in various fields of machine learning...
Deep neural networks have demonstrated outstanding performance in various fields of machine learning...
Parallel hardware accelerators, for example Graphics Processor Units, have limited on-chip memory ca...
In the wake of the success of convolutional neural networks in image classification, object recognit...