Optimizing Edge AI Models with High Performance Computing

Getting small by going big

Learn how AI models can be tailored to run efficiently on small, embedded devices. 

Limited in size, embedded devices offer numerous benefits such as reduced latency, lower costs, enhanced reliability, and independence from cloud providers. The main challenge lies in reducing the model's size to fit these compact devices without significantly compromising its accuracy. Through this course, you'll learn how high-performance computing (HPC) can be utilized to effectively shrink your AI models. We'll focus on using parallel hyperparameter optimization—a method that helps determine the best settings for AI models—to ensure they perform well even within the constraints of smaller hardware.

This event is part of the European funded RebootSkills project that aims to facilitate access for the manufacturing industry to high-class training in digital skills. 

Your trainer: Kurt De Grave

Dr. ir. Kurt De Grave is a senior researcher in artificial intelligence. He holds a…