diff options
| author | Nikhil Raj <nikhil.raj@arm.com> | 2021-06-16 11:06:52 +0100 |
|---|---|---|
| committer | Nikhil Raj Arm <nikhil.raj@arm.com> | 2021-06-16 14:22:57 +0000 |
| commit | 2ef580100c8de1bf8acea854607ac1e552e9703f (patch) | |
| tree | 47cc27df5da2803ffc6cc745cd1b5cf3b9e8067b | |
| parent | 53ef79504b4c881c572735393c2eede5fa556c46 (diff) | |
| download | armnn-2ef580100c8de1bf8acea854607ac1e552e9703f.tar.gz | |
Fix broken link to Ndk guide in armnn Readme.md
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Change-Id: I1e96c0b15721e522346e2b66f4e3d388225cebde
| -rw-r--r-- | README.md | 2 |
1 files changed, 1 insertions, 1 deletions
@@ -53,7 +53,7 @@ TfLite will then delegate operations, that can be accelerated with Arm NN, to Ar executed with the usual TfLite runtime. This is our **recommended way to accelerate TfLite models**. As with our parsers there are tutorials in our doxygen documentation that can be found in the [wiki section](https://github.com/ARM-software/armnn/wiki/Documentation). -If you would like to use **Arm NN on Android** you can follow this guide which explains [how to build Arm NN using the AndroidNDK](). +If you would like to use **Arm NN on Android** you can follow this guide which explains [how to build Arm NN using the AndroidNDK](BuildGuideAndroidNDK.md). But you might also want to take a look at another repository which implements a hardware abstraction layer (HAL) for Android. The repository is called [Android-NN-Driver](https://github.com/ARM-software/android-nn-driver) and when integrated into Android it will automatically run neural networks with Arm NN. |