In limited-resource edge computing circumstances such as on mobile devices, IoT devices,
and electric vehicles, the energy-efficient optimized convolutional neural network (CNN) …

This work presents a dynamically reconfigurable architecture for Neural Network (NN)
accelerators implemented in Field-Programmable Gate Array (FPGA) that can be applied in …

This article addresses the growing need in resource-constrained edge computing scenarios
for energy-efficient convolutional neural network (CNN) accelerators on mobile Field …

The use of artificial intelligence in healthcare applications offers significant accuracy and
utility for medical practitioners and patients. Deep learning has made a substantial positive …

A convolutional neural network (CNN) classifies images with high accuracy. However, CNN
operation requires a large number of computations which consume a significant amount of …

The performance of a computer vision system depends on the accuracy of visual information
extracted by the sensors and the system's visual-processing capabilities. To derive optimum …

Deep learning applications have achieved great success in numerous real-world
applications. Deep learning models, especially Convolution Neural Networks (CNN) are …

Although modern supercomputers are capable of delivering Exaflops now, they do not
always achieve their peak performance. For instance, even today's high-end supercom …

In deep neural network (DNN) accelerators, balancing computational performance and
energy efficiency is critical, particularly for deployment on resource-constrained platforms …

Convolutional neural networks (CNNs) are widely adopted in safety-critical systems,
including space applications and autonomous vehicles. Field-programmable gate arrays …