About This Training
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.