Название: Deep Learning and Edge Computing Solutions for High Performance ComputingАвтор: A. Suresh, Sara Paiva
Издательство: Springer
Год: 2021
Страниц: 286
Язык: английский
Формат: pdf (true), epub
Размер: 33.9 MB
High-Performance Computing (HPC) implies putting high-performance computing technologies into hardware systems deployable at the edge, where environmental conditions typically found in data center infrastructures are usually severe. To ensure stable and continuous operations in a wide range of temperatures and in demanding shock and vibration environments, a robust and lightweight design is required.
A new emergent domain for engineering is Edge Computing and Deep Learning. Comprehensive intelligent edge architectures and systems are needed for the next generation analytics powered by artificial intelligence (AI), machine learning (ML), and other high-performance workload processing.
High-performance computers have various specifications for development and challenges that cover hardware, software, networking, integration of platforms, and security. A few suppliers had the foresight and vision to start addressing HPC years ago and to shape a collaboration ecosystem with main suppliers of applications and technologies. However, those who did are now reaping the fruits of their work and have a drastic head start on rivals vying for several different sectors to take a share in a vital slice of the market.
In a range of uses, including computer vision and natural language processing, deep learning is currently widely used. End machines, such as smartphones and Internet-of-Things sensors, produce data that could be processed using deep learning in real time or used to train models of deep learning. However, inference and preparation for deep learning need significant computing resources to operate rapidly.
This book presents compelling evidence for the success of deep learning algorithms for edge computing as well as high-performance computing. The most important limitations as well as issues of these technologies’ phenomenon are meticulously addressed. This edited book provides a good and generalized background of the topic that quickly gives the reader an appreciation of the wide range of applications for these technologies.
- Identifies deep learning techniques in mobile edge data analytics and computing environments suitable for applications in healthcare;
- Introduces big data analytics to the sources available and possible challenges and techniques associated with bioinformatics and the healthcare domain;
- Features advancements in the computing field to effectively handle and make inferences from voluminous and heterogeneous healthcare data.
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