Add to ORKGThe aim of this master thesis is modeling of neural network accelerators with HW support for quantization. The thesis first focuses on the concept of computation in convolutional neural networks (CNNs) and introduces different categories of hardware architectures that are used for their processing. Following this, optimization techniques for CNN models are summarized, with the goal of achieving efficient processing on specialized hardware architectures. The subsequent part of the thesis involves a comparison of existing analytical tools that are used to estimate hardware performance parameters during inference and which can be expanded to incorporate quantization support. Based on an experimental comparison, the Timeloop tool was selected f...
With the surging popularity of edge computing, the need to efficiently perform neural network infere...
As machine learning algorithms play an ever increasing role in today's technology, more demands are ...
Neural network computing has attracted a lot of attention as it borrows the concept of human brain t...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...
With the evolution of machine learning algorithms they are seeing a wider use in traditional signal ...
Today, computer vision (CV) problems are solved with unprecedented accuracy using convolutional neur...
Convolutional neural networks (CNN) are state of the art machine learning models used for various co...
Convolutional Neural Networks (CNN) have become a popular solution for computer vision problems. How...
In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of t...
During the last years, Convolutional Neural Networks have been used for different applications thank...
Abstract Model quantization is a widely used technique to compress and accelerate deep neural netwo...
Convolution Neural Network (CNN) is a special kind of neural network that is inspired by the behavio...
The increasing use of machine learning algorithms, such as Convolutional Neural Networks (CNNs), mak...
International audienceThis paper compares the latency, accuracy, training time and hardware costs of...
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks....
With the surging popularity of edge computing, the need to efficiently perform neural network infere...
As machine learning algorithms play an ever increasing role in today's technology, more demands are ...
Neural network computing has attracted a lot of attention as it borrows the concept of human brain t...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...
With the evolution of machine learning algorithms they are seeing a wider use in traditional signal ...
Today, computer vision (CV) problems are solved with unprecedented accuracy using convolutional neur...
Convolutional neural networks (CNN) are state of the art machine learning models used for various co...
Convolutional Neural Networks (CNN) have become a popular solution for computer vision problems. How...
In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of t...
During the last years, Convolutional Neural Networks have been used for different applications thank...
Abstract Model quantization is a widely used technique to compress and accelerate deep neural netwo...
Convolution Neural Network (CNN) is a special kind of neural network that is inspired by the behavio...
The increasing use of machine learning algorithms, such as Convolutional Neural Networks (CNNs), mak...
International audienceThis paper compares the latency, accuracy, training time and hardware costs of...
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks....
With the surging popularity of edge computing, the need to efficiently perform neural network infere...
As machine learning algorithms play an ever increasing role in today's technology, more demands are ...
Neural network computing has attracted a lot of attention as it borrows the concept of human brain t...