EDGE AI FOUNDATION Courses

Empowering the Edge - Practical Applications of Embedded Machine Learning on MCUs

Discover practical implementations of machine learning on microcontrollers, from model compression techniques to real-world applications using Neural Processing Units for enhanced performance.

• YouTube
• 57 minutes
• Self-Paced
• Free Video

Online Learning TinyML for Anomaly Detection Based on Extreme Values Theory

Explore anomaly detection in TinyML through extreme value theory, focusing on implementation strategies for resource-constrained devices and time series analysis.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

On-Chip Learning and Implementation Challenges with Oscillatory Neural Networks

Explore cutting-edge oscillatory neural networks and their on-chip learning implementation challenges with insights from Professor Aida Todri-Sanial's technical presentation.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

NeuroMem - Ultra Low Power Hardwired Incremental Learning and Parallel Pattern Recognition

Discover how NeuroMem® technology enables ultra-low power pattern recognition and incremental learning through hardwired systems, presented by General Vision Holdings' co-founder.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

Merging Insights from Artificial and Biological Neural Networks for Neuromorphic Intelligence

Explore groundbreaking research on combining artificial and biological neural networks to advance neuromorphic intelligence, presented by Prof. Charlotte Frenkel of TU Delft.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Multi-armed Bandit on System-on-Chip - Go Frequentist or Bayesian?

Explore multi-armed bandit algorithms for edge computing, focusing on UCB, KLUCB, and Thompson Sampling implementations on System-on-Chip for IoT, wireless radio, and robotics applications.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

Infineon XENSIV 60GHz Radar Sensor and Development Kit for tinyML - 2023 Hackathon Challenge

Discover how to implement pedestrian detection using Infineon's XENSIV 60GHz radar sensor, exploring hardware features, rapid prototyping capabilities, and integration with ModusToolbox™ for IoT applications.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

Running and Managing Fleets of Single Board Computers at Scale for Machine Learning

Discover strategies for deploying and managing machine learning models across distributed single board computer networks, focusing on edge computing architectures, privacy, and real-world applications.

• YouTube
• 57 minutes
• Self-Paced
• Free Video

The Turbinator: A Contact-less Turbidity Sensor Utilizing Laser, Camera and Low Power Neural Network Processing

Discover how a patented contact-less turbidity sensor combines laser technology, camera imaging, and neural networks to monitor water quality through innovative low-power processing solutions.

• YouTube
• 56 minutes
• Self-Paced
• Free Video

Low Precision Inference and Training for Deep Neural Networks

Explore advanced techniques for optimizing deep learning applications using Block Minifloat arithmetic, focusing on low-precision inference and training methods for enhanced efficiency.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

tinyML Builds with Renesas

Explore practical tinyML development using Renesas microcontrollers, focusing on real-world applications and hands-on implementation techniques for edge AI solutions.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Neural Architecture Search for Tiny Devices - Optimizing Deep Neural Networks for Edge Platforms

Delve into automated neural architecture search techniques for edge devices, exploring optimization strategies for creating efficient, accurate deep learning models with minimal resource requirements.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Low-Energy Physiologic Biomarker Machine Learning Inference on a Wearable Device with GAP9 RISC-V Processor

Discover how Mayo Clinic develops machine learning biomarkers for wearable devices, from dataset creation to implementing low-energy prototypes using Greenwaves GAP9 processors and regenerative signal autoencoders.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Why TinyML Applications Fail - Common Challenges and Issues in Real-World Projects

Explore common pitfalls, challenges, and practical solutions in TinyML implementation for IoT edge applications, drawing from a decade of real-world commercial project experiences.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Arm Ethos-U Support in TVM ML Framework for IoT Devices

Discover how to accelerate ML computation on IoT devices using Arm Ethos-U microNPUs with TVM framework, enabling faster development and deployment of embedded machine learning applications.

• YouTube
• 21 minutes
• Self-Paced
• Free Video