Add to ORKGThis thesis provides an introduction to classical and convolutional neural networks. It describes how hardware multiplication is conventionally performed and optimized. A simplified multiplication method is proposed, namely multiplierless multiplication. This method is implemented and integrated into the TypeCNN library. The cost of the hardware solution of both conventional and simplified multipliers is estimated. The thesis also introduces software tools developed to work with convolutional neural networks and datasets used to test them in the image classification task. Test architectures and experimentation methodology are proposed. The results are evaluated, and both the classification accuracy and cost of the hardware solution are disc...
In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of t...
Convolutional neural networks, as most artificial neural networks, are frequently viewed as methods ...
Convolutional neural networks (CNNs) have achieved great success in image processing. However, the h...
Tato práce se zaměřuje na problematiku klasických i konvolučních neuronových sítí. Jsou zde probrány...
The breakthroughs in multi-layer convolutional neural networks (CNNs) have caused significant progre...
This thesis deals with convolutional neural networks. It is a kind of deep neural networks that are ...
Today, computer vision (CV) problems are solved with unprecedented accuracy using convolutional neur...
This article analyzes the effects of approximate multiplication when performing inferences on deep c...
Convolutional Neural Networks (CNNs) are hierarchical biologically-inspired models that may be taugh...
This paper proposes a low-cost approximate dynamic ranged multiplier and describes its use during th...
The subject of this thesis is neural network acceleration with the goal of reducing the number of fl...
Convolutional neural networks have been widely employed for image recognition applications because o...
In this work, a deterministic sequence suitable for approximate computing on stochastic computing ha...
In this paper, a design of a synthesizable hardware model for a Convolutional Neural Network (CNN) i...
In this work, we propose a multiplication-less deep convolution neural network, called BD-NET. As fa...
In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of t...
Convolutional neural networks, as most artificial neural networks, are frequently viewed as methods ...
Convolutional neural networks (CNNs) have achieved great success in image processing. However, the h...
Tato práce se zaměřuje na problematiku klasických i konvolučních neuronových sítí. Jsou zde probrány...
The breakthroughs in multi-layer convolutional neural networks (CNNs) have caused significant progre...
This thesis deals with convolutional neural networks. It is a kind of deep neural networks that are ...
Today, computer vision (CV) problems are solved with unprecedented accuracy using convolutional neur...
This article analyzes the effects of approximate multiplication when performing inferences on deep c...
Convolutional Neural Networks (CNNs) are hierarchical biologically-inspired models that may be taugh...
This paper proposes a low-cost approximate dynamic ranged multiplier and describes its use during th...
The subject of this thesis is neural network acceleration with the goal of reducing the number of fl...
Convolutional neural networks have been widely employed for image recognition applications because o...
In this work, a deterministic sequence suitable for approximate computing on stochastic computing ha...
In this paper, a design of a synthesizable hardware model for a Convolutional Neural Network (CNN) i...
In this work, we propose a multiplication-less deep convolution neural network, called BD-NET. As fa...
In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of t...
Convolutional neural networks, as most artificial neural networks, are frequently viewed as methods ...
Convolutional neural networks (CNNs) have achieved great success in image processing. However, the h...