Neural networks for industrial applications generally have additional constraints such as
response speed, memory size and power usage. Randomized learners can address some …

The capabilities of artificial neural networks are rapidly evolving, so are the expectations for
them to solve ever more challenging tasks in numerous everyday situations. Larger, more …

The convolutional neural network is a subfield of artificial neural networks and has made
great achievements in various domains over the past decade. The technique has been …

Convolutional Neural Networks (CNNs) for Artificial Intelligence (AI) algorithms have been
widely used in many applications especially for image recognition. However, the growth in …

Recently, due to the immense growth of computing power, image processing and
Convolutional neural networks (CNN) have regained gigantic attention because of the …

The number of CNN-based applications executing at the Edge has been considerably
increasing. Considering that CNNs are recognized error-resilient and the varied Edge …

State-of-the-art Convolutional Neural Networks (CNNs) have become increasingly accurate.
However, hundreds or thousands of megabytes data are involved to store them, making …

In today's technology era, Convolutional Neural Networks (CNNs) are the limelight for
various cognitive tasks because of their high accuracy. With the increasing complexity in the …

Approximate computing has emerged as an efficient design methodology for improving the
performance and power-efficiency of digital systems by allowing a negligible loss in the …

This paper provides a systematic mapping of the literature regarding automated Design
Space Exploration (DSE) for error-tolerant systems, where Approximate Computing (AxC) …