An FPGA architecture to emulate a single-layer Cellular Neural Network - Universal Machine (CNN-UM) is proposed. It is based on a fast realization of the CNN convolution operation on the parallel hardware of the FPGA. The setup is capable of performing a CNN iteration over a 30×30 pixel image in less than 30 μs. Moreover, this platform has been used to realize the visual system of an autonomous mobile robot. © 2007 IEEE
Recent years, with the development of Convolution Neural Networks (CNN), machine learning has achiev...
Cellular Neural Networks are characterized by simplicity of operation. The network consists of a lar...
In this paper, architecture of a Real-Time Cellular Neural Network (CNN) Processor (RTCNNP-v2) is gi...
Abstract — In this paper an FPGA based Implementation of a 1D-CNN with a 3×1 template and 8×1 length...
In order to get real time image processing for mobile robot vision, we propose to use a discrete tim...
Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capabil...
In previous works [1, 2] we developed a visual servoing platform using C language to extract the req...
Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the...
A new emulated digital multi-layer CNN-UM chip architecture called Falcon has been developed. Simula...
This paper deals with hardware implementation of Digital CNN network in FPGA. We have implemented us...
Convolutional Neural Network (CNN) has been extensively used for image recognition due to its great ...
Convolutional Neural Networks (CNNs) are a particular type of Artificial Neural Networks (ANNs) insp...
The paper investigates the potential for a packet switching network for real-time image processing b...
Convolutional Neural Networks (CNNs) are a very popular class of artificial neural networks. Current...
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of ...
Recent years, with the development of Convolution Neural Networks (CNN), machine learning has achiev...
Cellular Neural Networks are characterized by simplicity of operation. The network consists of a lar...
In this paper, architecture of a Real-Time Cellular Neural Network (CNN) Processor (RTCNNP-v2) is gi...
Abstract — In this paper an FPGA based Implementation of a 1D-CNN with a 3×1 template and 8×1 length...
In order to get real time image processing for mobile robot vision, we propose to use a discrete tim...
Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capabil...
In previous works [1, 2] we developed a visual servoing platform using C language to extract the req...
Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the...
A new emulated digital multi-layer CNN-UM chip architecture called Falcon has been developed. Simula...
This paper deals with hardware implementation of Digital CNN network in FPGA. We have implemented us...
Convolutional Neural Network (CNN) has been extensively used for image recognition due to its great ...
Convolutional Neural Networks (CNNs) are a particular type of Artificial Neural Networks (ANNs) insp...
The paper investigates the potential for a packet switching network for real-time image processing b...
Convolutional Neural Networks (CNNs) are a very popular class of artificial neural networks. Current...
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of ...
Recent years, with the development of Convolution Neural Networks (CNN), machine learning has achiev...
Cellular Neural Networks are characterized by simplicity of operation. The network consists of a lar...
In this paper, architecture of a Real-Time Cellular Neural Network (CNN) Processor (RTCNNP-v2) is gi...