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Convolutional neural network (CNN) has become a successful algorithm in the region of artificial intelligence and a strong candidate for many computer vision algorithms. But the computation complexity of CNN is much higher than traditional algorithms. With the help of GPU acceleration, CNN-based applications are widely deployed in servers. However, for embedded platforms, CNN-based solutions are still...
Sparsity helps reducing the computation complexity of DNNs by skipping the multiplication with zeros. The granularity of sparsity affects the efficiency of hardware architecture and the prediction accuracy. In this paper we quantitatively measure the accuracy-sparsity relationship with different granularity. Coarse-grained sparsity brings more regular sparsity pattern, making it easier for hardware...
Convolutional neural networks (CNNs) have recently broken many performance records in image recognition and object detection problems. The success of CNNs, to a great extent, is enabled by the fast scaling-up of the networks that learn from a huge volume of data. The deployment of big CNN models can be both computation-intensive and memory-intensive, leaving severe challenges to hardware implementations...
Deep learning, and especially Convolutional Neural Network (CNN, is among the most powerful and widely used techniques in computer vision. Applications range from image classification to object detection, segmentation, Optical Character Recognition (OCR), etc. At the same time, CNNs are both computationally intensive and memory intensive, making them difficult to be deployed on low power lightweight...
Computing nodes in reconfigurable clusters are occupied and released by applications during their execution. At compile time, application developers are not aware of the amount of resources available at run time. Dynamic Stencil is an approach that optimises stencil applications by constructing scalable designs which can adapt to available run-time resources in a reconfigurable cluster. This approach...
Nowadays, Graphics Processing Unit (GPU), as a kind of massive parallel processor, has been widely used in general purposed computing tasks. Although there have been mature development tools, it is not a trivial task for programmers to write GPU programs. Based on this consideration, we propose a novel parallel computing architecture. The architecture includes a parallel programming model, named Gemma,...
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