Current Trainings

We cordially invite you to join our booth event on »Unlock the Chiplet Technology Now! – Chiplet Development and Applications from an European Perspective« at embedded world on March 11, 2026, 4 p.m. Together with representatives from leading companies in the European chiplet ecosystem, we will explore the current challenges companies are facing regarding innovative chiplet technology in Europe. Enjoy impulse talks as well as the opportunity for networking with our chiplet experts. Secure one of the limited free tickets and discover how the advancements of the chiplet approach can also transform your business. We look forward to welcoming you in Nuremberg at booth 422 in hall 4! Event language: English

The increasing complexity and cost sensitivity of modern semiconductor components pose significant challenges for the industry. In this webinar, you will learn how innovative chiplet approaches and heterogeneous integration technologies lay the foundation for cutting-edge solutions. We will demonstrate how new design methodologies enable the combination of different design domains across technological options to meet both economic and performance requirements. You will discover how to ensure that your heterogeneous system accounts for all relevant effects, adheres to target cost figures, and maximizes performance. This webinar provides practical insights into optimizing complex systems and informs you about strategies to successfully tackle technological and economic challenges. In this webinar, we will introduce you to the core principles of Active Learning and explore state-of-the-art AL strategies for image and time-series datasets. Using two practical examples – wood defect detection and tool wear detection in milling machines – we will demonstrate how Active Learning can streamline data annotation while simultaneously improving model accuracy. To conclude, we will present a demonstrator showcasing the entire AL pipeline in practice. The demonstrator iteratively trains AI models from scratch using Active Learning. This workflow highlights how Active Learning reduces labeling effort, accelerates convergence, and demonstrates both efficiency and impact in real-world scenarios.

Active Learning (AL) is a machine learning approach that significantly reduces the amount of required training data while maintaining or even improving the performance of the AI model. During the learning process, an AI model selects data points with the highest informational value, which are then labeled by a human. This targeted labeling approach greatly reduces manual effort and, consequently, the costs of model development, particularly in areas such as quality monitoring and predictive maintenance. In this webinar, we will introduce you to the core principles of Active Learning and explore state-of-the-art AL strategies for image and time-series datasets. Using two practical examples – wood defect detection and tool wear detection in milling machines – we will demonstrate how Active Learning can streamline data annotation while simultaneously improving model accuracy. To conclude, we will present a demonstrator showcasing the entire AL pipeline in practice. The demonstrator iteratively trains AI models from scratch using Active Learning. This workflow highlights how Active Learning reduces labeling effort, accelerates convergence, and demonstrates both efficiency and impact in real-world scenarios.