DL4IoT 2024 Extended Abstracts

The use of AI in IoT applications dramatically extends the capabilities of IoT systems and their application areas. These new, next-generation IoT architectures allow for processing vast amounts of data collected in distributed systems, affecting areas like transportation (e.g. self-driving cars), automation (e.g. robotization or predictive maintenance), and our homes (e.g. assisted living). Teaching the IoT to learn imposes a number of new challenges. New hardware and accelerator concepts are required for efficient data processing in a distributed environment. Also, significant improvements on the toolchain are required to bridge between AI and IoT developers. Furthermore, security, privacy, or robustness for such systems becomes a critical challenge, being essential for most application domains.

The Deep Learning for IoT (DL4IoT) workshop aims at discussing challenges and solutions related to the exploitation of artificial intelligence (AI) and deep learning (DL) for handling the large complexity of IoT applications.

This repository contains the extended abstracts of presentations given at the 3rd edition of the DL4IoT workshop, which took place on January 19, 2024, with HiPEAC 2024, in Munich, Germany.

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