Название: Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence
Автор: George K. Thiruvathukal, Yung-Hsiang Lu
Издательство: CRC Press
Год: 2022
Страниц: 436
Язык: английский
Формат: pdf (true)
Размер: 22.3 MB

Deep Neural Networks (DNNs) are greatly successful in performing many different computer vision tasks. However, the state-of-the-art DNNs are too energy, computation, and memory-intensive to be deployed on most computing devices and embedded systems. DNNs usually require server-grade CPUs and GPUs. To make computer vision more ubiquitous, recent research has focused on making DNNs more efficient. These techniques make DNNs smaller and faster through various refinements and thus are enabling computer vision on battery-powered mobile devices. Through this article, we survey the recent progress in low-power Deep Learning to discuss and analyze the advantages, limitations, and potential improvements to the different techniques. We particularly focus on the software-based techniques for low-power DNN inference. This survey classifies the energy-efficient DNN techniques into six broad categories: (1)Quantization, (2)Pruning, (3)Layer and Filter Compression, (4)Matrix Decomposition, (5)Neural Architecture Search, and (6)Knowledge Distillation. The techniques in each category are discussed in greater detail in this book.

Take-aways
- Surveys the recent progress in low-power deep learning to analyze the advantages, limitations, and potential improvements to the different techniques.
- Focus on the software-based techniques for low-power DNN inference

The powerful deep neural networks (DNNs) have been propelling the development of efficient computer vision technologies for mobile systems such as phones and drones. To enable power-efficient image processing on resource-constrained devices, many studies have been dedicated to the field of low-power DNNs from different layers of the systems. Amongst the deep stack of low-power DNN systems, task scheduling also plays an essential role as the interfacing middleware between the algorithms and the underlying hardware. Especially when heterogeneous SoCs have been widely adopted in edge and mobile scenarios as the hardware solution, an efficient DNN task scheduler is needed to reduce the implementation overhead of DNN-based task and extract the most power from the SoC platform.

Designing efficient neural network architectures is a widely adopted approach to improve efficiency, besides compressing an existing deep neural network. A CNN (Convolutional Neural Network ) model typically consists of convolution layers, pooling layers, and fully-connected layers, where most of the computation comes from convolution layers.

On device AI has become increasingly important for reasons of latency, privacy and overall autonomy as computing becomes more and more ambient. Moreover, making AI, in particular computer vision, efficient and run well in low resource computing environments using frameworks like PyTorch is a priority of the industry to enable this. The IEEE Low-Power Computer Vision Challenge is one such effort that has and continues to push the field forward allowing us to make progress in this area. Facebook has been a proud sponsor and supporter of this challenge since 2018 and this book presents the winners' solutions from previous challenges and can guide researchers, engineers, and students to design efficient on device AI. -- Joe Spisak, Product Lead at Facebook Artificial Intelligence

Computer vision is at the center of recent breakthroughs in artificial intelligence. Being able to process visual data in low-power computing environments will enable great advances in the field in areas such as edge computing and Internet of Things. This book presents work by experts in the field and their winning solutions. It is an indispensable resource for anyone interested creating AI technologies in resource constrained computing environments. -- Mark Liao, Director, Institute of Information Science, Academia Sinica