Add to ORKGSGS-2019-3017We compare different platforms for inference of convolutional neural networks in this paper. We trained various neural networks to determine the material in the source hyperspectral cube. Then we convert them to inference format and compare the inference results. We used tools under Xilinx Vitis AI for FPGA implementation. We try to minimize the size of the proposed networks by pruning them and provide further comparisons. FPGA platforms show to be energy efficient but still slower than a graphics card in terms of performance
Convolutional Neural Networks (CNNs) have exhibited certain human-like performance on computer visio...
Convolutional Neural Networks (CNNs) are becoming increasingly popular in deep learning applications...
Convolutional Neural Networks (CNNs) are nowadays ubiquitously used in a wide range of applications....
SGS-2019-3017We compare different platforms for inference of convolutional neural networks in this...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...
The development of machine learning has made a revolution in various applications such as object det...
When asked to implement a neural network application, the decision concerning what hardware platform...
Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capabil...
Convolutional Neural Network (CNN) is a type of algorithm used to solve complex problems with a supe...
While providing the same functionality, the various Deep Learning software frameworks available thes...
A convolutional neural network (CNN) is a deep learning framework that is widely used in computer vi...
Edge devices are becoming smarter with the integration of machine learning methods, such as deep lea...
International audienceThe success of Deep Learning (DL) algorithms in computer vision tasks have cre...
In recent years, research in the space community has shown a growing interest in Artificial Intellig...
abstract: Machine learning is a powerful tool for processing and understanding the vast amounts of d...
Convolutional Neural Networks (CNNs) have exhibited certain human-like performance on computer visio...
Convolutional Neural Networks (CNNs) are becoming increasingly popular in deep learning applications...
Convolutional Neural Networks (CNNs) are nowadays ubiquitously used in a wide range of applications....
SGS-2019-3017We compare different platforms for inference of convolutional neural networks in this...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...
The development of machine learning has made a revolution in various applications such as object det...
When asked to implement a neural network application, the decision concerning what hardware platform...
Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capabil...
Convolutional Neural Network (CNN) is a type of algorithm used to solve complex problems with a supe...
While providing the same functionality, the various Deep Learning software frameworks available thes...
A convolutional neural network (CNN) is a deep learning framework that is widely used in computer vi...
Edge devices are becoming smarter with the integration of machine learning methods, such as deep lea...
International audienceThe success of Deep Learning (DL) algorithms in computer vision tasks have cre...
In recent years, research in the space community has shown a growing interest in Artificial Intellig...
abstract: Machine learning is a powerful tool for processing and understanding the vast amounts of d...
Convolutional Neural Networks (CNNs) have exhibited certain human-like performance on computer visio...
Convolutional Neural Networks (CNNs) are becoming increasingly popular in deep learning applications...
Convolutional Neural Networks (CNNs) are nowadays ubiquitously used in a wide range of applications....