In this brief, we introduce an architecture for accelerating convolution stages in convolutional
neural networks (CNNs) implemented in embedded vision systems. The purpose of the …

Convolutional neural networks (CNNs) are a vital approach in machine learning. However,
their high complexity and energy consumption make them challenging to embed in mobile …

Deep convolutional neural networks (DCNNs) have become one of the most popular
approaches to many visual processing tasks. The majority of existing works on the …

Developing efficient embedded vision applications requires exploring various algorithmic
optimization trade-offs and a broad spectrum of hardware architecture choices. This makes …

Convolutional Neural Networks (CNNs) have proven to be extremely accurate for image
recognition, even outperforming human recognition capability. When deployed on battery …

To enable efficient deployment of convolutional neural networks (CNNs) on embedded
platforms for different computer vision applications, several convolution variants have been …

Convolutional neural networks (CNNs) have been widely deployed in computer vision tasks.
However, the computation and resource intensive characteristics of CNN bring obstacles to …

Customized hardware based convolutional neural network (CNN or ConvNet) accelerators
have attracted significant attention for applications in a low-cost, edge computing system …

In recent years, neural network accelerators have been shown to achieve both high energy
efficiency and high performance for a broad application scope within the important category …

To realize deep learning techniques, a type of deep neural network (DNN) called a
convolutional neural networks (CNN) is among the most widely used models aimed at …