Accelerating ML Compute for IoT/Embedded Market

Machine learning is impacting every market including deeply embedded applications. Intelligence is rapidly moving to endpoint devices with increasing demand to enable ML in devices that are the most resource-constrained. Security, latency and bandwidth issues for audio and video use-cases demand ever-increasing processing capability in endpoint devices. This presentation will talk about how AI applications and Neural Networks can be efficiently implemented and accelerated on the extremely low-power, low-area endpoint devices. The talk will also show Arm€™s collaborative approach, utilizing open source software for ML solutions. This approach will enable developers to efficiently tackle the challenges in this space and make it easy to create and deploy ML applications.