About This Upcoming Webinar
Market demands for user privacy and security, along with a need for lower latency, are pushing more and more processing capability to the edge. The increasing prevalence of ML in industrial and IoT edge applications is driving demand for higher performance in embedded devices within the power constraints.
This presentation will explore the acceleration of machine learning on low power and resource-constrained embedded systems using the Arm Ethos-U65 microNPU. It will also show how an open source and collaborative software approach will enable developers to easily create and deploy ML applications.
Not sure you can join? You should still register! Slides and a recording of the session will be available after the webinar.