Machine learning is a very important topic in the electronic design community and lately more and more applications for machine learning and deep learning systems have been developed. Thus, this course will focus on the hardware implementation of neuronal networks and deep learning systems and, in three days, it promises the participants to learn about neuronal networks and their hardware implementation, analog circuit design concepts to enable neuronal networks, neuromorphic computing architectures and deep learning systems.
Conducted by Professor Giacomo Indiveri from the Institute for Neuroinformatics of the University of Zurich (Switzerland) and Professor Verhelst Department ESAT. KU Leuven (Belgium), the course is open to engineers, IC designers and engineering manager who are interested on the hardware implementation of neuronal networks and deep learning systems, but also to people who work on the software implementation of machine learning and artificial intelligence algorithms. Notice that some knowledge in CMOS technology and digital IC design is required to follow this course.