A significant demand rises for energy-efficient deep neural networks to support power-
limited embedding devices with successful deep learning applications in IoT and edge …

Abstract “How much energy is consumed for an inference made by a convolutional neural
network (CNN)?” With the increased popularity of CNNs deployed on the wide-spectrum of …

This article presents PreVIous, a methodology to predict the performance of convolutional
neural networks (CNNs) in terms of throughput and energy consumption on vision-enabled …

With the development of the architectures and the growth of AIoT application requirements,
data processing on edge has become popular. Neural network inference is widely employed …

Convolutional Neural Networks (CNNs) are increasingly becoming popular in embedded
and energy limited mobile applications. Hardware designers have proposed various …

Internet of Things (IoT) and edge devices have grown in their application fields due to
machine learning (ML) models and their capacity to classify images into previously known …

Deep Neural Networks (DNN) are widely deployed in resource-limited edge devices. Due to
the limitation of computational resources, it is important to meet the timing and energy …

Convolutional neural networks (CNNs) are widely used in various pattern recognition and
computer vision applications due to their state-of-the-art performance. This superior …

Deep learning has achieved accuracy and fast training speed and has been successfully
applied to many fields, including speech recognition, text processing, image processing and …

The amalgamation of artificial intelligence with Internet of Things (AIoT) devices have seen a
rapid surge in growth, largely due to the effective implementation of deep neural network …