Add to ORKGThe execution of multi-inference tasks on low-powered edge devices has become increasingly popular in recent years for adding value to data on-device. The focus of the optimization of such jobs has been on hardware, neural network architectures, and frameworks to reduce execution speed. However, it is yet not known how different scheduling policies affect the execution speed of a multi-inference job. An empirical study has been performed to investigate the effects of scheduling policies on multi-inference. The execution performance information of multi-inference batch jobs under combinations of loading and scheduling policies were determined under varying levels of constrained memory. These results were obtained using EdgeCaffe: a framework...
International audienceFuture sixth-generation (6G) networks will rely on the synergies of edge compu...
Recent developments in Artificial Intelligence (AI) research enable new strategies for running Machi...
Deep learning models have replaced conventional methods for machine learning tasks. Efficient infere...
Deep neural networks (DNNs) are becoming the core components of many applications running on edge de...
The increasingly growing expansion of the Internet of Things (IoT) along with the convergence of mul...
Recent advances in both lightweight deep learning algorithms and edge computing increasingly enable ...
A plethora of applications are using machine learning, the operations of which are becoming more com...
Computer science and engineering have evolved rapidly over the last decade offering innovative Machi...
With the advancement of machine learning, a growing number of mobile users rely on machine learning ...
INST: L_042Edge computing is an essential technology to enable machine learning capabilities on IoT ...
Deep neural networks (DNNs) are becoming the core components of many applications running on edge de...
Thesis (Master's)--University of Washington, 2021With the advancement of machine learning (ML), a gr...
For time-critical IoT applications using deep learning, inference acceleration through distributed c...
In recent years, machine learning applications are progressing on mobile systems for enhanced user ...
Inference carried out on pre-trained deep neural networks (DNNs) is particularly effective as it doe...
International audienceFuture sixth-generation (6G) networks will rely on the synergies of edge compu...
Recent developments in Artificial Intelligence (AI) research enable new strategies for running Machi...
Deep learning models have replaced conventional methods for machine learning tasks. Efficient infere...
Deep neural networks (DNNs) are becoming the core components of many applications running on edge de...
The increasingly growing expansion of the Internet of Things (IoT) along with the convergence of mul...
Recent advances in both lightweight deep learning algorithms and edge computing increasingly enable ...
A plethora of applications are using machine learning, the operations of which are becoming more com...
Computer science and engineering have evolved rapidly over the last decade offering innovative Machi...
With the advancement of machine learning, a growing number of mobile users rely on machine learning ...
INST: L_042Edge computing is an essential technology to enable machine learning capabilities on IoT ...
Deep neural networks (DNNs) are becoming the core components of many applications running on edge de...
Thesis (Master's)--University of Washington, 2021With the advancement of machine learning (ML), a gr...
For time-critical IoT applications using deep learning, inference acceleration through distributed c...
In recent years, machine learning applications are progressing on mobile systems for enhanced user ...
Inference carried out on pre-trained deep neural networks (DNNs) is particularly effective as it doe...
International audienceFuture sixth-generation (6G) networks will rely on the synergies of edge compu...
Recent developments in Artificial Intelligence (AI) research enable new strategies for running Machi...
Deep learning models have replaced conventional methods for machine learning tasks. Efficient infere...