This paper describes the design and the implementation of an embedded system based on multiple FPGAs that can be used to process real time video streams in standalone mode for applications that require the use of large Multi-Layer CNNs (ML-CNNs). The system processes video in progressive mode and provides a standard VGA output format. The main features of the system are determined by using a distributed computing architecture, based on Independent Hardware Modules (IHM), which facilitate system expansion and adaptation to new applications. Each IHM is composed by an FPGA board that can hold one or more CNN layers. The total computing capacity of the system is determined by the number of IHM used and the amount of resources available in the ...
Machine learning has become ubiquitous and penetrated every field of technology, medicine, and finan...
Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the...
A new digital VLSI architecture has been presented for the implementation of discrete-time multilaye...
Abstract — This paper describes the design and the implementation of an embedded system based on mul...
A new emulated digital multi-layer CNN-UM chip architecture called Falcon has been developed. Simula...
In previous works [1, 2] we developed a visual servoing platform using C language to extract the req...
Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capabil...
This paper describes a novel architecture for the hardware implementation of non-linear multi-layer ...
Vision systems are an integral part of our society and continue to fuel many areas of research and d...
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...
In this paper, architecture of a Real-Time Cellular Neural Network (CNN) Processor (RTCNNP-v2) is gi...
The predictive power of Convolutional Neural Networks (CNNs) has been an integral factor for emergin...
Abstract — In this paper an FPGA based Implementation of a 1D-CNN with a 3×1 template and 8×1 length...
Machine learning has become ubiquitous and penetrated every field of technology, medicine, and finan...
Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the...
A new digital VLSI architecture has been presented for the implementation of discrete-time multilaye...
Abstract — This paper describes the design and the implementation of an embedded system based on mul...
A new emulated digital multi-layer CNN-UM chip architecture called Falcon has been developed. Simula...
In previous works [1, 2] we developed a visual servoing platform using C language to extract the req...
Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capabil...
This paper describes a novel architecture for the hardware implementation of non-linear multi-layer ...
Vision systems are an integral part of our society and continue to fuel many areas of research and d...
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...
In this paper, architecture of a Real-Time Cellular Neural Network (CNN) Processor (RTCNNP-v2) is gi...
The predictive power of Convolutional Neural Networks (CNNs) has been an integral factor for emergin...
Abstract — In this paper an FPGA based Implementation of a 1D-CNN with a 3×1 template and 8×1 length...
Machine learning has become ubiquitous and penetrated every field of technology, medicine, and finan...
Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the...
A new digital VLSI architecture has been presented for the implementation of discrete-time multilaye...