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25 - 27 February 2020 // Nuremberg, Germany

Conferences and supporting programme

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Session 8.4: Intelligent Systems IV Development Methods

Designing Intelligent Systems Using Resource Constrained Edge Devices Vortragssprache Englisch

Traditional embedded software engineers often think that machine learning and intelligent systems are outside the realm of microcontroller based systems and therefore outside their realm of expertise. Advances in microcontroller technology have made designing intelligent systems using these resource constrained devices a reality. In this session, we will examine the tools and capabilities that are available to microcontroller designers to start using machine learning and adding a new level of intelligence to their devices. This session will introduce machine learning and what is required to utilize it in a Cortex-M environment. We'll explore key concepts such as DSP, hardware floating point accelerators, software libraries and capabilities that are available within the arm ecosystem to create machine learning capabilities. We'll examine architectures and requirements for training in the cloud and deploying that training onto the resource constrained device. We will look at examples on how machine learning can be used to monitor system behavior, watch for security threats, detect failing sensors and other potential applications. *Key Takeaways - Machine learning can be deployed on Cortex-M processors - The tools and techniques needed to use machine learning on a Cortex-M - Ideas and design patterns that can be used on microcontroller based devices - Potential applications for edge nodes that can utilize machine learning

--- Date: 27.02.2019 Time: 16:30 - 17:00 Location: Conference Counter NCC Ost



Jacob Beningo

Beningo Embedded Group


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