Machine learning for embedded systems at the edge by NXP & Arm

Register today for upcoming Arm Tech Talks: https://www.arm.com/techtalks Get ready for another one of our Arm Tech Talks! Every fortnight, we discuss and explore some of the latest trends, technologies and best practices in the world of AI, featuring partners from the AI Ecosystem as well as speakers across Arm. Machine learning inference is impacting a wide range of markets and devices, especially low power microcontrollers and power-constrained devices for IoT applications. These devices can often only consume milliwatts of power, and therefore not achieve the traditional power requirements of cloud-based approaches. By performing inference on-device, ML can be enabled on these IoT endpoints delivering greater responsiveness, security and privacy while reducing network energy consumption, latency and bandwidth usage. This talk between Arm and NXP's MCU product managers and engineers will explain how developers can efficiently implement and accelerate ML on extremely low-power, low-area Cortex-M based devices with open-source software libraries and tools. The discussion will include a demo on the i.MX RT1060 crossover MCU to show how to create and deploy ML applications at the edge. Speakers: Anthony Huereca, Systems Engineer, Edge Processing, NXP Kobus Marneweck, Senior Product Manager, Arm