Managing random hardware faults requires the faults to be detected online, thus simplifying
recovery. Algorithm-based fault tolerance has been proposed as a low-cost mechanism to …

Convolution is one of the most critical operations in various application domains and its
computation should combine high performance with energy efficiency. This requirement is …

Long-Short Term Memory (LSTM) networks, and Recurrent Neural Networks (RNNs) in
general, have demonstrated their suitability in many time series data applications, especially …

It is sometimes desirable to implement filters using a transpose-form filter structure.
However, managing image borders is generally considered more complex than it is with the …

Convolutional Neural Networks (CNNs) have been utilised in many image and video
processing applications. The convolution operator, also known as a spatial filter, is usually a …

This research delves into sophisticated neural network frameworks like Convolutional
Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory …

One of the most important difficulties which doctors face in diagnosing is the analysis and
diagnosis of brain stroke in magnetic resonance imaging (MRI) images. Brain stroke is the …

Convolution is the central computation kernel for various machine learning applications. The
convolution stride controls the number of pixels by which the kernel's window moves after …

There is a need to evaluate the resource usage and optimize the performance of the floating-
point 2D convolution kernels on a recent FPGA which features large numbers of hardened …

Abstract CMOS Image Sensors (CIS) are crucial components in multimedia applications for
the Internet of the Things (IoT). They allow IoT devices to perceive, interpret, and respond to …