• views: 149
• date: 8 September 2025
• posted by: 0nelove

What you'll learn
Learn the Full Workflow of Tiny Machine Learning Model on Embedded Devices
Understand How Testor Flow Lite for Microcontroller (TFLM) Library will be parse and run the Machine Learning Model underence on your embedded device
Understand the Standard Machine Learning Models Limitations on Embedded Systems and the needs to have different and optimized flow for Limited Resources Devices
Learn How ARM had helped to create and define dedicated hardware , architectures and compiler to allow Machine Larning Model Inference on embedded devices
You will get to lear ARM based Machine Learning Hardware Accelerators families (Ethos-U) and associated System On Chip Design Integration of those Accelerators
Requirements
You should have some understanding of embedded systems based devices and their limitations
Some basic understanding of ARM based architectures and System integration
Description
Machine Learning for Embedded Systems with ARM Ethos-UAre 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 learnCore ML theory for embedded devicesUnderstand 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 workflowTrain 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 devicesSee 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 hardwareWork 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 uniqueBridges 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!
Who this course is for
Embedded Systems developers who wants to start learning Machine Learning for embedded devices



   
   
   

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