# Get started with Scalable Vector Extension 2 on Android

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android_sve2/)
- [Enable SVE2 support in Android Studio](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android_sve2/part1/)
- [Implement vector operations](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android_sve2/part2/)
- [Next Steps](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android_sve2/_next-steps/)

## About this Learning Path

| Skill level:   | Introductory   |
|----------------|----------------|
| Reading time:  | 40 min         |
| Last updated:  | 05 Aug 2026    |

| Author:         | Dawid Borycki [GitHub](https://github.com/dawidborycki) |
|-----------------|-------------------------------------------------------|
| Arm IP:         | [Cortex-A](https://support.arm.com/?tab=compute-ip&Product%20Type=Application%20Processors) |
| Tags:           | [Performance and Architecture](https://learn.arm.com/tag/performance-and-architecture), [Android](https://learn.arm.com/tag/android), [Android Studio](https://learn.arm.com/tag/android-studio) |

### Who is this for?

This is an introductory topic for software developers interested in learning how to use the Scalable Vector Extension 2 (SVE2) on Arm powered mobile devices running Android.

### What will you learn?

Upon completion of this Learning Path, you will be able to:

- Enable Scalable Vector Extension 2 (SVE2) support in Android Studio.
- Implement an Android application that uses the Android Native Development Kit (NDK) to calculate the fused multiply-add (FMA).
- Measure the performance uplift by using SVE2 intrinsics.

### Prerequisites

Before starting, you will need the following:

- A x86_64 or Apple development machine with Android Studio installed.
- A 64-bit Arm powered smartphone running Android.
- Knowledge of Single instruction Multi Data (SIMD).
- Knowledge of [Neon](https://developer.arm.com/documentation/102474/latest).
- Knowledge of [Scalable Vector Extension (SVE)](https://developer.arm.com/documentation/101726/4-0).

### Summary

You’ll enable SVE2 in an Android Studio project with the Android NDK, then implement and benchmark a fused multiply-add operation. First, you’ll add native C++ helpers, create two FMA implementations, and time them on a 64-bit Arm Android device. Then, you’ll compare the results to validate a minimal SVE2 example and measure its performance difference.

### Frequently asked questions

<details>
<summary>Where do I add the SVE2 intrinsics and helper code?</summary>
Edit `native-lib.cpp` under `app/cpp/`. Add the necessary includes, helper functions, both FMA implementations with and without SVE2, and the `measureExecutionTime` template in that file.
</details>

<details>
<summary>How do I know SVE2 support is enabled in Android Studio?</summary>
Your project should compile SVE2 intrinsics without errors and build successfully for your target. After you rebuild, run the app to execute both code paths and obtain timing results.
</details>

<details>
<summary>What result should I expect from the two fused multiply-add (FMA) implementations?</summary>
Both FMA implementations should return the same output. The FMA implementation with SVE2 should compute the result 3 to 4 times faster than the FMA without SVE2, depending on vector length.
</details>

<details>
<summary>How many iterations should I pass to `measureExecutionTime`?</summary>
Choose a value large enough to get stable timings on your device, then keep it the same for both implementations. The function returns a duration you can compare directly between the SVE2 and non-SVE2 runs.
</details>

<details>
<summary>What should I check if the project fails to build after enabling SVE2?</summary>
Verify you edited the correct source file and included the headers listed in the steps. Then, sync and rebuild the project to apply the configuration changes.
</details>