Special Issue on integration of machine learning and edge computing for next generation of smart wearable systems

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-10-30 DOI:10.1016/j.future.2024.107574

Paolo Gastaldo , Edoardo Ragusa , Strahinja Dosen , Francesco Palmieri

引用次数: 0

Abstract

Machine learning (ML) provides an enabling technology for the development of the next generation of smart devices. However, the integration of ML and edge computing faces major challenges. While powerful models can tackle difficult tasks such as visual recognition or natural language processing, the constrained resources of embedded systems might prevent direct deployment of the designed inference function into an edge device. This Special Issue collects manuscripts describing methodologies and systems that tackle the integration of ML into embedded systems. The focus is on solutions that can stimulate significant improvements across different domains.

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为下一代智能可穿戴系统整合机器学习和边缘计算特刊

机器学习（ML）为下一代智能设备的开发提供了有利技术。然而，ML 与边缘计算的整合面临着重大挑战。虽然强大的模型可以解决视觉识别或自然语言处理等困难任务，但嵌入式系统资源有限，可能无法将设计好的推理功能直接部署到边缘设备中。本特刊收集了介绍将 ML 集成到嵌入式系统的方法和系统的手稿。重点关注可促进不同领域显著改进的解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.

期刊最新文献

Identifying runtime libraries in statically linked linux binaries High throughput edit distance computation on FPGA-based accelerators using HLS In silico framework for genome analysis Adaptive ensemble optimization for memory-related hyperparameters in retraining DNN at edge Convergence-aware optimal checkpointing for exploratory deep learning training jobs