# 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

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

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| Author: | Dawid Borycki<br>[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?
After completing this Learning Path, you'll be able to perform the following tasks:
- 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'll need the following prerequisites:
- 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

**Where do I add the SVE2 intrinsics and helper code?**

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.

**How do I know SVE2 support is enabled in Android Studio?**

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.

**What result should I expect from the two fused multiply-add (FMA) implementations?**

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.

<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.

**What should I check if the project fails to build after enabling SVE2?**

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.