Deep Learning (DL) algorithm deployment on edge devices, such as Convolutional Neural Network (CNN) inference, has established a high computing demand on devices with limited resources, requiring low execution time and reduced energy consumption. To meet the requirements with such constraints, hardware systems have adopted unconventional processors co-located on the same platform. This architectural heterogeneity introduces many challenges in how these processors interact. A well defined software-hardware co-design environment must be carefully built to ensure a high-performance solution. For this purpose, heterogeneous hardware-awareness must be integrated in the designworkflow.To avoid hardware-agnostic low performance programming, state-...
This thesis explores Convolutional Neural Network (CNN) inference accelerator architecture for FPGAs...
This contribution presents the performance modeling of a super desktop with GPU and FPGA accelerator...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...
Le déploiement d’algorithmes tel que l’inférence de réseaux de neurones convolutifs, impose des temp...
Presented at DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous ...
The rapid innovation of neural network algorithms has led to neural network architectures with more ...
Deep Convolutional Neural Networks (CNNs) have become a de-facto standard in computer vision. This s...
International audienceThe success of Deep Learning (DL) algorithms in computer vision tasks have cre...
This thesis presents the results of an architectural study on the design of FPGA- based architecture...
Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the...
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of ...
The increasing use of machine learning algorithms, such as Convolutional Neural Networks (CNNs), mak...
Being one of the cutting-edge solutions in the computer vision field, Convolutional neural networks ...
Les réseaux de neurones convolutifs (CNN) sont largement utilisés dans le domaine la reconnaissance ...
This thesis explores Convolutional Neural Network (CNN) inference accelerator architecture for FPGAs...
This contribution presents the performance modeling of a super desktop with GPU and FPGA accelerator...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...
Le déploiement d’algorithmes tel que l’inférence de réseaux de neurones convolutifs, impose des temp...
Presented at DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous ...
The rapid innovation of neural network algorithms has led to neural network architectures with more ...
Deep Convolutional Neural Networks (CNNs) have become a de-facto standard in computer vision. This s...
International audienceThe success of Deep Learning (DL) algorithms in computer vision tasks have cre...
This thesis presents the results of an architectural study on the design of FPGA- based architecture...
Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the...
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of ...
The increasing use of machine learning algorithms, such as Convolutional Neural Networks (CNNs), mak...
Being one of the cutting-edge solutions in the computer vision field, Convolutional neural networks ...
Les réseaux de neurones convolutifs (CNN) sont largement utilisés dans le domaine la reconnaissance ...
This thesis explores Convolutional Neural Network (CNN) inference accelerator architecture for FPGAs...
This contribution presents the performance modeling of a super desktop with GPU and FPGA accelerator...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...