In this article, we survey existing academic and commercial efforts to provide Field-
Programmable Gate Array (FPGA) acceleration in datacenters and the cloud. The goal is a …

Abstract 3D convolution neural networks (CNNs) have shown excellent predictive
performance on tasks such as action recognition from videos. Since 3D CNNs have unique …

Neural networks (NNs) have demonstrated their potential in a variety of domains ranging
from computer vision (CV) to natural language processing. Among various NNs, two …

Pruning has been widely used for deep neural network optimization and compression. In
this paper we propose a pruning method to accelerate FPGA implementations of neural …

Convolutional neural network (CNN) is used efficiently for the classification of hyperspectral
imagery (HSI). Both 3-D CNN and hybrid 2D–3D CNN have better performance due to the …

Three-dimensional (3-D) understanding or inference has received increasing attention,
where 3-D convolutional neural networks (3D-CNNs) have demonstrated superior …

Convolutional neural networks (CNNs) have had great success when applied to computer
vision technology, and many application-specific integrated circuit (ASIC) and field …

Three-dimensional convolutional neural networks (3D CNNs) have proven to be outstanding
in applications such as video analysis, 3-dimension geometric data, and 3-dimension …

The hardware design of 3D Convolution Neural Networks (CNNs) requires massive
compute and memory due to an additional temporal dimension. This paper explores various …

3-Dimensional (3D) convolutional neural networks (CNN) are widely used in the field of
disparity estimation. However, 3D CNN is more computationally dense than 2D CNN due to …