Don’t miss | Optimized Machine Learning on resource-constrained devices webinar

 

. . . . Live Webinar: . Develop ML Applications with the Glow Neural Network Compiler and TensorFlow Lite on i.MX RT Crossover MCUs . Register Now » . . . September 2, 2020 | 60-minute webinar Machine learning is a rapidly growing field, especially as deep learning moves to the edge and more and more engineers build applications that include some form of vision- or voice-based machine learning technology. TensorFlow Lite, as an inference engine, is one such example of a deep learning tool that has gained tremendous popularity in recent years. A relative newcomer to this field is Glow, the open source neural network compiler. This session explores the trade-offs and features of TensorFlow Lite and the Glow NN compiler, now available within NXP’s MCUXpresso SDK, with a focus on how to target these technologies for MCUs, to work within the various resource constraints such as memory and power inherent in MCUs. Join us to attend and participate in the live questions and answers session. Featured Speakers: Markus Levy, NXP Director of AI and Machine Learning Technologies, NXP Semiconductors Anthony Huereca, NXP systems engineer focused on embedded MCUs and machine learning on the edge, NXP Semiconductors . Additional Resources eIQ™ ML Software Development environment | Read more » MCUXpresso Ecosystem | Read more » i.MX RT600, i.MX RT1050, i.MX RT1060 Crossover MCUs Glow Compiler Optimizes Neural Networks for Low-Power NXP MCUs | Read more » . . Develop ML applications with the Glow neural network compiler . September . 2 . . . 10:00 AM PDT . Register Now » . . . .