This paper presents a convolutional neural network (CNN) accelerator that can skip zero weights and handle outliers, which are few but have a significant impact on the accuracy of CNNs, to achieve speedup and increase the energy efficiency of CNN. We propose an offline weight-scheduling algorithm which can skip zero weights and combine two non-outlier weights simultaneously using bit-level sparsity of CNNs. We use a reconfigurable multiplier-and-accumulator (MAC) unit for two purposes; usually used to compute combined two non-outliers and sometimes to compute outliers. We further improve the speedup of our accelerator by clipping some of the outliers with negligible accuracy loss. Compared to DaDianNao [7] and Bit-Tactical [16] architecture...
Deep Neural Networks (DNN) have reached an outstanding accuracy in the past years, often going beyon...
Convolutional neural networks (CNNs) have achieved great success in image processing. However, the h...
Convolutional Neural Network (CNN) are widely used in the field of computer vision and show its grea...
Convolutional neural networks (CNNs) are one of the most successful machine-learning techniques for ...
DNNs have been finding a growing number of applications including image classification, speech recog...
Sparsity – the presence of many zero values – is a pervasive property of modern deep neural networks...
Convolutional neural networks (CNNs) outperform traditional machine learning algorithms across a wid...
Doctor of PhilosophyDepartment of Computer ScienceArslan MunirDeep neural networks (DNNs) have gaine...
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. ...
Owing to the presence of large values, which we call outliers, conventional methods of quantization ...
Convolutional neural networks (CNNs) are computationally intensive, and deep learning hardware shoul...
High computational complexity and large memory footprint hinder the adoption of convolution neural n...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
There is great attention to develop hardware accelerator with better energy efficiency, as well as t...
Convolutional Neural Networks (CNN) have become a popular solution for computer vision problems. How...
Deep Neural Networks (DNN) have reached an outstanding accuracy in the past years, often going beyon...
Convolutional neural networks (CNNs) have achieved great success in image processing. However, the h...
Convolutional Neural Network (CNN) are widely used in the field of computer vision and show its grea...
Convolutional neural networks (CNNs) are one of the most successful machine-learning techniques for ...
DNNs have been finding a growing number of applications including image classification, speech recog...
Sparsity – the presence of many zero values – is a pervasive property of modern deep neural networks...
Convolutional neural networks (CNNs) outperform traditional machine learning algorithms across a wid...
Doctor of PhilosophyDepartment of Computer ScienceArslan MunirDeep neural networks (DNNs) have gaine...
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. ...
Owing to the presence of large values, which we call outliers, conventional methods of quantization ...
Convolutional neural networks (CNNs) are computationally intensive, and deep learning hardware shoul...
High computational complexity and large memory footprint hinder the adoption of convolution neural n...
Over the last ten years, the rise of deep learning has redefined the state-of-the-art in many comput...
There is great attention to develop hardware accelerator with better energy efficiency, as well as t...
Convolutional Neural Networks (CNN) have become a popular solution for computer vision problems. How...
Deep Neural Networks (DNN) have reached an outstanding accuracy in the past years, often going beyon...
Convolutional neural networks (CNNs) have achieved great success in image processing. However, the h...
Convolutional Neural Network (CNN) are widely used in the field of computer vision and show its grea...