| title | Overview |
|---|---|
| weight | 2 |
| layout | learningpathall |
Selecting the best hardware for machine learning (ML) models depends on effective tools. You can visualize ML performance early in the development cycle by using Arm Fixed Virtual Platforms (FVPs).
This Learning Path uses TinyML. TinyML is machine learning tailored to function on devices with limited resources, constrained memory, low power, and fewer processing capabilities.
For a learning path focused on creating and deploying your own TinyML models, please see Introduction to TinyML on Arm using PyTorch and ExecuTorch
New products, like Arm's Ethos-U85 NPU are available on FVPs earlier than on physical devices. FVPs also have a graphical user interface (GUI), which is useful for for ML performance visualization due to:
- visual confirmation that your ML model is running on the desired device,
- clearly indicated instruction counts,
- confirmation of total execution time and
- visually appealing output for prototypes and demos.