Convolutional neural networks (CNNs) outperform traditional machine learning algorithms across a wide range of applications, such as object recognition, image segmentation, and autonomous driving. However, their ever-growing computational complexity makes it necessary to design efficient hardware accelerators. Most CNN accelerators focus on exploring various dataflow styles and designs that exploit computational parallelism. However, potential performance improvement (speedup) from sparsity has not been adequately addressed. The computation and memory footprint of CNNs can be significantly reduced if sparsity is exploited in network evaluations. To further improve performance and energy efficiency, some accelerators evaluate CNNs with limit...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
open4siDeep neural networks have achieved impressive results in computer vision and machine learning...
In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage ...
High computational complexity and large memory footprint hinder the adoption of convolution neural n...
The inherent sparsity present in convolutional neural networks (CNNs) offers a valuable opportunity ...
DNNs have been finding a growing number of applications including image classification, speech recog...
Convolutional neural networks (CNNs) are often pruned to achieve faster training and inference speed...
This paper presents a convolutional neural network (CNN) accelerator that can skip zero weights and ...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
Deep neural network models are commonly used in various real-life applications due to their high pre...
Doctor of PhilosophyDepartment of Computer ScienceArslan MunirDeep neural networks (DNNs) have gaine...
Sparse convolutional neural network (CNN) models reduce the massive compute and memory bandwidth req...
Mobile devices are becoming an important carrier for deep learning tasks, as they are being equipped...
This work is focused on the pruning of some convolutional neural networks (CNNs) and improving their...
This thesis explores Convolutional Neural Network (CNN) inference accelerator architecture for FPGAs...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
open4siDeep neural networks have achieved impressive results in computer vision and machine learning...
In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage ...
High computational complexity and large memory footprint hinder the adoption of convolution neural n...
The inherent sparsity present in convolutional neural networks (CNNs) offers a valuable opportunity ...
DNNs have been finding a growing number of applications including image classification, speech recog...
Convolutional neural networks (CNNs) are often pruned to achieve faster training and inference speed...
This paper presents a convolutional neural network (CNN) accelerator that can skip zero weights and ...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
Deep neural network models are commonly used in various real-life applications due to their high pre...
Doctor of PhilosophyDepartment of Computer ScienceArslan MunirDeep neural networks (DNNs) have gaine...
Sparse convolutional neural network (CNN) models reduce the massive compute and memory bandwidth req...
Mobile devices are becoming an important carrier for deep learning tasks, as they are being equipped...
This work is focused on the pruning of some convolutional neural networks (CNNs) and improving their...
This thesis explores Convolutional Neural Network (CNN) inference accelerator architecture for FPGAs...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
open4siDeep neural networks have achieved impressive results in computer vision and machine learning...
In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage ...