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Название: Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Use Cases and Emerging ChallengesАвтор: Sudeep Pasricha, Muhammad Shafique
Издательство: Springer
Год: 2024
Страниц: 571
Язык: английский
Формат: pdf (true)
Размер: 28.9 MB
Machine Learning (ML) has emerged as a prominent approach for achieving state-of-the-art accuracy for many data analytic applications, ranging from computer vision (e.g., classification, segmentation, and object detection in images and video), speech recognition, language translation, healthcare diagnostics, robotics, and autonomous vehicles to business and financial analysis. The driving force of the ML success is the advent of Neural Network (NN) algorithms, such as Deep Neural Networks (DNNs)/Deep Learning (DL) and Spiking Neural Networks (SNNs), with support from today’s evolving computing landscape to better exploit data and thread-level parallelism with ML accelerators. Current trends show an immense interest in attaining the powerful abilities of NN algorithms for solving ML tasks using embedded systems with limited compute and memory resources, i.e., so-called Embedded ML. One of the main reasons is that embedded ML systems may enable a wide range of applications, especially the ones with tight memory and power/energy constraints, such as mobile systems, Internet of Things (IoT), edge computing, and cyber-physical applications. Furthermore, embedded ML systems can also improve the quality of service (e.g., personalized systems) and privacy as compared to centralized ML systems.
