Estimate neural accelerator (NX) performance with ML Inference Advisor
Introduction
Understand MLIA and the NX Performance Estimator
Set up your MLIA environment
Download a public baseline model
Check baseline operator compatibility
Estimate performance and inspect configurations
Compare a smaller model under the same target profile
(Optional) Run and profile the model on an NX device
Next Steps
Estimate neural accelerator (NX) performance with ML Inference Advisor
Introduction
Understand MLIA and the NX Performance Estimator
Set up your MLIA environment
Download a public baseline model
Check baseline operator compatibility
Estimate performance and inspect configurations
Compare a smaller model under the same target profile
(Optional) Run and profile the model on an NX device
Next Steps
Who is this for?
This Learning Path is for ML engineers and graphics developers who want to evaluate model compatibility and estimated performance for Arm Neural Technology before deployment.
What will you learn?
After completing this Learning Path, you'll be able to perform the following tasks:
- Install the public MLIA Neural Technology plugin and discover its target profiles and backend
- Analyze public quantized MobileNet models with automatic model conversion
- Check operator compatibility and interpret NX Performance Estimator reports
- Compare model variants under the same target configuration and identify accuracy tradeoffs
Prerequisites
Before starting, you'll need the following prerequisites:
- An Ubuntu development host
- Basic familiarity with Python, command-line tools, and model deployment
- (Optional) An NX-capable Android device