Machine Learning for Embedded Systems with ARM Ethos-U NPU
Machine Learning for Embedded Systems with ARM Ethos-U NPU, Learn AI, ML, and TensorFlow Lite for microcontrollers with ARM NPU.
Course Description
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
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
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
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
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
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!
