Who is this for?
This is an advanced topic for developers and performance engineers who deploy ExecuTorch models on Arm devices and want to understand and reduce inference latency.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Understand how SME2 acceleration changes the performance profile of ExecuTorch models by reducing compute-bound bottlenecks
- Interpret operator-level and operator-category breakdowns (for example, convolution, GEMM, data movement, and other operators)
- Identify which operators benefit most from SME2 acceleration and which operators become the new performance bottlenecks
- Apply a model-agnostic profiling workflow that you reuse across different models and deployments
- Make evidence-based optimization decisions by comparing execution profiles with SME2 enabled and disabled
Prerequisites
Before starting, you will need the following:
- An Apple Silicon macOS host with Python 3.9 or later and CMake 3.29 or later
- Basic familiarity with ExecuTorch or PyTorch
- Optionally, an Android device with Armv9 and SME2 support for on-device testing (if used, configure power management settings to ensure consistent performance measurements)
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
.pte. You’ll run timing-only and trace-enabled passes to capture operator and category breakdowns for convolution, GEMM, and data movement. Then, you’ll compare the profiles to identify SME2 gains and new bottlenecks, and use structured agent skills to automate and validate the workflow.Frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
.pte model and two ExecuTorch runner binaries, one with SME2 enabled and one with SME2 disabled. Complete model-specific input and output handling during onboarding.executorch/examples/models/efficient_sam as a model-onboarding example. It demonstrates the full process, including input and output details, and provides a template for your model.