Use of machine learning algorithms in IoT is proliferating dramatically. Multisensory context awareness, natural human to machine interfaces, and decision-making in various disciplines such as mechanical fault detection and personal healthcare are just a few examples of applications that would not be possible without these algorithms. The technology is migrating from traditional cloud-based services to local devices for better efficiency, autonomy and privacy. The multitude of artificial neural network classes and constantly increasing model complexity present a number of challenges for systems developers. This session presents approaches to solving the challenges of using machine learning technologies in low-power IoT devices.
Jianying Peng, R&D Manager, Synopsys