A new emulated digital multi-layer CNN-UM chip architecture called Falcon has been developed. Simulation running time can be hundred times shorter using the Falcon processor array compared to the software simulation. This huge computing power makes real time image processing possible. In this paper the main steps of the FPGA implementation and optimization are introduced. The Distributed Arithmetic technique is used to optimize the architecture on FPGAs. Using this technique, smaller and faster arithmetic units can be designed than using conventional approach where multiplier cores and adder trees are used to compute the state equation of the CNN array
In previous works [1, 2] we developed a visual servoing platform using C language to extract the req...
This paper presents a CMOS implementation of a layered CNN concurrent with 32times32 photosensors wi...
Cellular Neural Networks are characterized by simplicity of operation. The network consists of a lar...
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...
This paper describes a novel architecture for the hardware implementation of non-linear multi-layer ...
This thesis presents the results of an architectural study on the design of FPGA- based architecture...
This paper describes the design and the implementation of an embedded system based on multiple FPGAs...
An FPGA architecture to emulate a single-layer Cellular Neural Network - Universal Machine (CNN-UM) ...
Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a gr...
The predictive power of Convolutional Neural Networks (CNNs) has been an integral factor for emergin...
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of ...
Deep Convolutional Neural Networks (CNNs) have become a de-facto standard in computer vision. This s...
Abstract — In this paper an FPGA based Implementation of a 1D-CNN with a 3×1 template and 8×1 length...
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...
This paper presents a CMOS implementation of a layered CNN concurrent with 32times32 photosensors wi...
Cellular Neural Networks are characterized by simplicity of operation. The network consists of a lar...
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...
This paper describes a novel architecture for the hardware implementation of non-linear multi-layer ...
This thesis presents the results of an architectural study on the design of FPGA- based architecture...
This paper describes the design and the implementation of an embedded system based on multiple FPGAs...
An FPGA architecture to emulate a single-layer Cellular Neural Network - Universal Machine (CNN-UM) ...
Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a gr...
The predictive power of Convolutional Neural Networks (CNNs) has been an integral factor for emergin...
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of ...
Deep Convolutional Neural Networks (CNNs) have become a de-facto standard in computer vision. This s...
Abstract — In this paper an FPGA based Implementation of a 1D-CNN with a 3×1 template and 8×1 length...
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...
This paper presents a CMOS implementation of a layered CNN concurrent with 32times32 photosensors wi...
Cellular Neural Networks are characterized by simplicity of operation. The network consists of a lar...