Machine Learning for Embedded Systems with ARM Ethos-U

Are you ready to bring the power of machine learning into the world of embedded systems?
This course takes you on a complete, hands-on journey from building and training models to running them on real ARM-based hardware with dedicated NPUs.

Most ML courses stop at theory or training. This one goes further: you’ll actually deploy and run models on embedded devices, bridging the gap between machine learning and practical engineering.

What you’ll learn

The core ML theory behind embedded AI

• Understand the stages of a neural network execution pipeline

• Explore convolution, flattening, activation functions, and softmax in CNNs

• Learn how ML operations are optimized for resource-constrained devices

Understand the stages of a neural network execution pipeline

Explore convolution, flattening, activation functions, and softmax in CNNs

Learn how ML operations are optimized for resource-constrained devices

Model preparation workflow

• Train models in TensorFlow

• Convert them into lightweight .tflite models

• Optimize and compile with the ARM Vela compiler for the Ethos-U NPU

Train models in TensorFlow

Convert them into lightweight .tflite models

Optimize and compile with the ARM Vela compiler for the Ethos-U NPU

Running inference on embedded devices

• Execute models with TensorFlow Lite Micro (TFLM) in C++

• See how ML operations map to CMSIS-NN kernels and the Ethos-U hardware accelerator

• Understand the complete inference path — from model to silicon

Execute models with TensorFlow Lite Micro (TFLM) in C++

See how ML operations map to CMSIS-NN kernels and the Ethos-U hardware accelerator

Understand the complete inference path — from model to silicon

Hands-on with real hardware

• Set up and run the Alif E7 ML Development Kit

• Build and deploy Keyword Spotting and Image Classification demos

• Observe real-time outputs directly on the device

Set up and run the Alif E7 ML Development Kit

Build and deploy Keyword Spotting and Image Classification demos

Observe real-time outputs directly on the device

Why this course is unique

• Bridges the gap between ML theory and real embedded deployment

• Covers the entire workflow — from training to NPU execution

• Practical, hardware-driven approach using the Alif E7 ML dev kit

• Projects designed for easy reproduction on a Windows machine

Bridges the gap between ML theory and real embedded deployment

Covers the entire workflow — from training to NPU execution

Practical, hardware-driven approach using the Alif E7 ML dev kit

Projects designed for easy reproduction on a Windows machine

By the end of this course, you’ll have the confidence and skills to run ML models efficiently on modern embedded systems, skills that are in high demand across IoT, robotics, and edge AI applications.

Whether you’re an embedded engineer ready to add AI to your skill set, or a machine learning practitioner eager to deploy models on hardware accelerators, this course will give you a competitive edge in the future of AI and embedded systems.

Enroll now and start building the next generation of embedded AI applications!