Add to ORKGPlenty of edge deep learning accelerators are proposed to speed up the inference of deep learning algorithms on edge devices. However, Various edge deep learning accelerators feature different characteristics in terms of power and performance, which makes it a very challenging task to compare different accelerators according to their specifications and in turn prohibits a new DL model from being effectively and efficiently deployed on a suitable edge device. We introduce EDLAB, an edge deep learning accelerator benchmark tool, to evaluate the overall performance of edge deep learning accelerators. EDLAB is an end-to-end benchmark tool that provides unified workloads, deployment policy, and fair comparison methodology. Moreover, EDLAB is des...
The recent “Cambrian explosion” of Deep Learning (DL) algorithms in concert with the end of Moore’s ...
Deep learning has risen to prominence in fields from medicine to autonomous vehicles. This rise has ...
Deep neural networks (DNNs) have succeeded in many different perception tasks, e.g., computer vision...
Edge TPUs are a domain of accelerators for low-power, edge devices and are widely used in various Go...
© 2022 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more inform...
Most real-time computer vision applications, such as pedestrian detection, augmented reality, and vi...
The ability to accurately predict deep neural network (DNN) inference performance metrics, such as l...
Deep neural networks (DNNs) have been increasingly deployed on and integrated with edge devices, suc...
Current applications that require processing of large amounts of data, such as in healthcare, trans...
For the past decade, machine learning (ML) has revolutionized numerous domains in our daily life. No...
Deep learning has demonstrated high accuracy and efficiency in various applications. For example, Co...
In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial ...
In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to their unma...
Optimising deep learning inference across edge devices and optimisation targets such as inference ti...
Embedded systems are becoming interconnected and collaborative systems able to perform autonomous ta...
The recent “Cambrian explosion” of Deep Learning (DL) algorithms in concert with the end of Moore’s ...
Deep learning has risen to prominence in fields from medicine to autonomous vehicles. This rise has ...
Deep neural networks (DNNs) have succeeded in many different perception tasks, e.g., computer vision...
Edge TPUs are a domain of accelerators for low-power, edge devices and are widely used in various Go...
© 2022 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more inform...
Most real-time computer vision applications, such as pedestrian detection, augmented reality, and vi...
The ability to accurately predict deep neural network (DNN) inference performance metrics, such as l...
Deep neural networks (DNNs) have been increasingly deployed on and integrated with edge devices, suc...
Current applications that require processing of large amounts of data, such as in healthcare, trans...
For the past decade, machine learning (ML) has revolutionized numerous domains in our daily life. No...
Deep learning has demonstrated high accuracy and efficiency in various applications. For example, Co...
In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial ...
In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to their unma...
Optimising deep learning inference across edge devices and optimisation targets such as inference ti...
Embedded systems are becoming interconnected and collaborative systems able to perform autonomous ta...
The recent “Cambrian explosion” of Deep Learning (DL) algorithms in concert with the end of Moore’s ...
Deep learning has risen to prominence in fields from medicine to autonomous vehicles. This rise has ...
Deep neural networks (DNNs) have succeeded in many different perception tasks, e.g., computer vision...