Hardware support for deep convolutional neural networks (CNNs) is critical to advanced
computer vision in mobile and embedded devices. Current designs, however, accelerate …

Deep convolutional neural networks (CNNs) are rapidly becoming the dominant approach to
computer vision and a major component of many other pervasive machine learning tasks …

Convolutional neural networks (CNNs) are used to solve many challenging machine
learning problems. Interest in CNNs has led to the design of CNN accelerators to improve …

The rapid pace and successful application of machine learning research and development
has seen widespread deployment of deep convolutional neural networks (CNNs). Alongside …

Convolutional Neural Networks (CNN) are very computation-intensive. Recently, a lot of
CNN accelerators based on the CNN intrinsic parallelism are proposed. However, we …

Hardware acceleration of Deep Neural Networks (DNNs) aims to tame their enormous
compute intensity. Fully realizing the potential of acceleration in this domain requires …

Deep Convolutional Neural Networks (CNNs) perform billions of operations for classifying a
single input. To reduce these computations, this paper offers a solution that leverages a …

Unlike standard Convolutional Neural Networks (CNNs) with fully-connected layers, Fully
Convolutional Neural Networks (FCN) are prevalent in computer vision applications such as …

With Convolutional Neural Networks (CNN) becoming more of a commodity in the computer
vision field, many have attempted to improve CNN in a bid to achieve better accuracy to a …

Convolutional Neural Networks (CNNs) have begun to permeate all corners of electronic
society (from voice recognition to scene generation) due to their high accuracy and machine …