With the advent of the Internet of Everything, the proliferation of data has put a huge burden
on data centers and network bandwidth. To ease the pressure on data centers, edge …

Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown
remarkable performance in most computer vision tasks. These tasks traditionally use a fixed …

Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

Deep convolutional neural network (DCNN) based supervised learning is a widely practiced
approach for large-scale image classification. However, retraining these large networks to …

As convolutional neural networks (CNNs) enable state-of-the-art computer vision
applications, their high energy consumption has emerged as a key impediment to their …

The increasing computational cost of deep neural network models limits the applicability of
intelligent applications on resource-constrained edge devices. While a number of neural …

Embedded devices are generally small, battery-powered computers with limited hardware
resources. It is difficult to run deep neural networks (DNNs) on these devices, because …

Energy efficiency is critical for running computer vision on battery-powered systems, such as
mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the …

Computer vision on low-power edge devices enables applications including search-and-
rescue and security. State-of-the-art computer vision algorithms, such as Deep Neural …

In this brief, we present a new approach to optimize energy efficiency of object detection
tasks using semantic decomposition to build a hierarchical classification framework. We …