Add to ORKGField-programmable gate array (FPGA) based accelerators are being widely used for acceleration of convolutional neural networks (CNNs) due to their potential in improving the performance and reconfigurability for specific application instances. To determine the optimal configuration of an FPGA-based accelerator, it is necessary to explore the design space and an accurate performance prediction plays an important role during the exploration. This work introduces a novel method for fast and accurate estimation of latency based on a Gaussian process parametrised by an analytic approximation and coupled with runtime data. The experiments conducted on three different CNNs on an FPGA-based accelerator on Intel Arria 10 GX 1150 demonstrated a 30.7...
High computational complexity and large memory footprint hinder the adoption of convolution neural n...
In the past few years we have experienced an extremely rapid growth of modern applications based on ...
Convolutional Neural Networks (CNNs) are becoming increasingly popular in deep learning applications...
Contemporary advances in neural networks (NNs) have demonstrated their potential in different appli...
This thesis explores Convolutional Neural Network (CNN) inference accelerator architecture for FPGAs...
During the last years, convolutional neural networks have been used for different applications, than...
abstract: The rapid improvement in computation capability has made deep convolutional neural network...
Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a gr...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of ...
The development of machine learning has made a revolution in various applications such as object det...
The increasing use of machine learning algorithms, such as Convolutional Neural Networks (CNNs), mak...
Convolutional neural network (CNN) has been widely employed for image recognition because it can ach...
This thesis presents the results of an architectural study on the design of FPGA- based architecture...
During the last years, Convolutional Neural Networks have been used for different applications thank...
High computational complexity and large memory footprint hinder the adoption of convolution neural n...
In the past few years we have experienced an extremely rapid growth of modern applications based on ...
Convolutional Neural Networks (CNNs) are becoming increasingly popular in deep learning applications...
Contemporary advances in neural networks (NNs) have demonstrated their potential in different appli...
This thesis explores Convolutional Neural Network (CNN) inference accelerator architecture for FPGAs...
During the last years, convolutional neural networks have been used for different applications, than...
abstract: The rapid improvement in computation capability has made deep convolutional neural network...
Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a gr...
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of proble...
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a wide range of ...
The development of machine learning has made a revolution in various applications such as object det...
The increasing use of machine learning algorithms, such as Convolutional Neural Networks (CNNs), mak...
Convolutional neural network (CNN) has been widely employed for image recognition because it can ach...
This thesis presents the results of an architectural study on the design of FPGA- based architecture...
During the last years, Convolutional Neural Networks have been used for different applications thank...
High computational complexity and large memory footprint hinder the adoption of convolution neural n...
In the past few years we have experienced an extremely rapid growth of modern applications based on ...
Convolutional Neural Networks (CNNs) are becoming increasingly popular in deep learning applications...