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Low Code Development of a TinyML Predictive Maintenance Solution

Oct 20
16:00 - 16:50


We're going to explain how to quickly develop an anomaly detection solution using the Arduino Portenta hardware and the Arduino software framework.

Arduino has championed low-code to enable high productivity development of solutions. In this talk we're going to show the step-by-step creation of a device that can be coupled with a piece of industrial equipment, trained in-device, set up to detect anomalies perceived through sensor data, and record events through our cloud platform.



Accelerating Safety and Security Verification of Arm v9-based Systems to the Pre-silicon Phase

Frank Schirrmeister

Senior Group Director, Solutions & Ecosystem / Cadence Design Systems

Maxwell Hinson

Lead Technical Marketing Engineer / Green Hills Software

Simultaneously designing and testing safety and security of both software and hardware before silicon is available has become a necessity to meet today’s market demands. This presentation illustrates methods for concurrent HW/SW co-development for...

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Environmental Data Acquisition and Processing

Gabriel Kiarie

Research Intern / Dedan Kimathi University of Technology

Ecosystems around the world are suffering from degradation due to human activity. To determine ecosystems at risk, it is important to monitor these ecosystems continuously. In this workshop we aim to demonstrate two technologies, namely acoustic m...

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Running Neural Networks in a Real-time Game Engine

Alexandre Ribard

Machine Learning Engineer / Unity

Neural networks have brought significant improvements to many areas of real-time 3D, such as scene construction, gameplay, rendering and so on. However, deploying neural networks across different consumer devices at an interactive framerate is not ye...

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