Machine Learning for Embedded Systems with ARM Ethos-U

Are you ready to bring the power of machine learning to the world of embedded systems? This course gives you a complete, hands-on journey into how modern AI models — like CNNs for vision and audio tasks — can be deployed efficiently on ARM-based platforms with dedicated NPUs.

Unlike most machine learning courses that stop at training, here you will go end-to-end, from model design all the way to running inference on real embedded hardware.

What you’ll learn

• Core ML theory for embedded devices

  • Understand the key stages of a neural network execution pipeline.

  • Learn the roles of convolution, flattening, activation functions, and softmax in CNNs.

  • Build a strong foundation in how ML operations are optimized for resource-constrained devices.

• Model preparation workflow

  • Train your model in TensorFlow.

  • Convert it to a lightweight .tflite model.

  • Optimize and compile it with the ARM Vela compiler to generate instructions for the Ethos-U NPU.

• Running inference on embedded devices

  • See how the TensorFlow Lite Micro (TFLM) runtime executes models in C++.

  • Understand how ML operations are dispatched to CMSIS-NN kernels and the Ethos-U hardware accelerator for maximum efficiency.

  • Get a clear picture of the full inference path from model to silicon.

• Hands-on with real hardware

  • Work with the Alif E7 ML development kit to put theory into practice.

  • Step through board setup and boot.

  • Explore the Alif E7 block diagram to understand its ML-capable architecture.

  • Clone, build, and deploy Keyword Spotting and Image Classification demos.

  • Run the models on the board and observe real-time outputs.

Core ML theory for embedded devices

• Understand the key stages of a neural network execution pipeline.

• Learn the roles of convolution, flattening, activation functions, and softmax in CNNs.

• Build a strong foundation in how ML operations are optimized for resource-constrained devices.

Understand the key stages of a neural network execution pipeline.

Learn the roles of convolution, flattening, activation functions, and softmax in CNNs.

Build a strong foundation in how ML operations are optimized for resource-constrained devices.

Model preparation workflow

• Train your model in TensorFlow.

• Convert it to a lightweight .tflite model.

• Optimize and compile it with the ARM Vela compiler to generate instructions for the Ethos-U NPU.

Train your model in TensorFlow.

Convert it to a lightweight .tflite model.

Optimize and compile it with the ARM Vela compiler to generate instructions for the Ethos-U NPU.

Running inference on embedded devices

• See how the TensorFlow Lite Micro (TFLM) runtime executes models in C++.

• Understand how ML operations are dispatched to CMSIS-NN kernels and the Ethos-U hardware accelerator for maximum efficiency.

• Get a clear picture of the full inference path from model to silicon.

See how the TensorFlow Lite Micro (TFLM) runtime executes models in C++.

Understand how ML operations are dispatched to CMSIS-NN kernels and the Ethos-U hardware accelerator for maximum efficiency.

Get a clear picture of the full inference path from model to silicon.

Hands-on with real hardware

• Work with the Alif E7 ML development kit to put theory into practice.

• Step through board setup and boot.

• Explore the Alif E7 block diagram to understand its ML-capable architecture.

• Clone, build, and deploy Keyword Spotting and Image Classification demos.

• Run the models on the board and observe real-time outputs.

Work with the Alif E7 ML development kit to put theory into practice.

Step through board setup and boot.

Explore the Alif E7 block diagram to understand its ML-capable architecture.

Clone, build, and deploy Keyword Spotting and Image Classification demos.

Run the models on the board and observe real-time outputs.

Why this course is unique

• Bridges the gap between machine learning theory and embedded deployment.

• Covers the complete workflow from training to NPU execution — not just pieces in isolation.

• Demonstrates everything on a real ARM-based platform with AI acceleration.

• Practical, hardware-driven approach using the Alif E7 ML dev kit with projects you can reproduce on a Windows machine.

Bridges the gap between machine learning theory and embedded deployment.

Covers the complete workflow from training to NPU execution — not just pieces in isolation.

Demonstrates everything on a real ARM-based platform with AI acceleration.

Practical, hardware-driven approach using the Alif E7 ML dev kit with projects you can reproduce on a Windows machine.

Whether you are an embedded engineer looking to break into AI, or a machine learning practitioner curious about deploying on hardware accelerators, this course will give you the knowledge and practical skills to run ML models efficiently on modern embedded systems.

Enroll now and start your journey into embedded machine learning with ARM Ethos-U!