ADR Awareness Centre

Presentation detailing our benchmarking of several low cost boards for AI inference on the Edge.
The NN is a small NN that performs 1 channel images classification over 58 categories targeting an industrial application.

Edge AI, which refers to the deployment of edge computing and artificial intelligence (AI) models and algorithms on edge devices like IoT devices and embedded systems, is rapidly evolving with several emerging trends and engineering principles app

Develop new electronic components and systems, processing architectures, connectivity, software, algorithms, and middleware through the combination of microelectronics, AI, embedded systems, and edge computing.

The automotive industry faces
unprecedented challenges, such as
climate change, reducing emissions,
global competition, and rapidly evolving
technologies such as digitalisation

This webinar is organised by the Chips JU “Edge AI Technologies for Optimized
Performance Embedded Processing” EdgeAI project, aiming at delivering an in-depth

The webinar introduces NXP Semiconductors’ edge AI platforms, software environment for application development, and model-zoo, offering validated starting points for edge AI projects.

The webinar brings together European and international open-source experts to share best practices, explore key trends, challenges, and benefits, and discuss collaboration on AI within the Open Source Initiative and the Eclipse Foundation.

The webinar covers the challenges, advantages, and limitations of edge AI technology, using various examples to illustrate its applications.

In this exclusive webinar, we dive deep into the exciting world of generative AI for Internet of Things (IoT) applications, focusing on how to fine-tune advanced AI models for Vision-Informed Text (VIT).

Offers a comprehensive library of free resources to help integrate Generative AI into educational settings.

This is deliverable 1.2 of the AI4REALNET project.

This is the deliverable 2.2 of the AI4REALNET project. This work package objective consists on developing the AI fundamental blocks for augmented decision-making, namely novel AI algorithms for large-scale sequential decision-making problems.