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author | Nikhil Raj <nikhil.raj@arm.com> | 2021-02-22 14:40:39 +0000 |
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committer | Keith Davis <keith.davis@arm.com> | 2021-02-22 18:02:20 +0000 |
commit | 5c6764c4f0c6cad83fc94f993426ea7edd7f6f42 (patch) | |
tree | 65ccac3fd3ae3d754cdb21de465865d6401d3aed /README.md | |
parent | d6bd29e968e777aa1c3f45b39b9aa79eb7eafbd9 (diff) | |
download | armnn-5c6764c4f0c6cad83fc94f993426ea7edd7f6f42.tar.gz |
Minor doc updates
* Updating README
* Updating android ndk build guide
Change-Id: I09f5e4fe09d590b6b482800730b78cbc84cee9e9
Signed-off-by: Nikhil Raj <nikhil.raj@arm.com>
Diffstat (limited to 'README.md')
-rw-r--r-- | README.md | 2 |
1 files changed, 1 insertions, 1 deletions
@@ -49,7 +49,7 @@ usability by providing Arm NN as a **Debian package**. Our debian package is the the TfLite Parser and PyArmNN (More support is about to come): [Installation via Apt Repository](InstallationViaAptRepository.md) The newest member in Arm NNs software toolkit is the **TfLite Delegate**. The delegate can be integrated in TfLite. -TfLite will then delegate operations that can be accelerated with Arm NN to Arm NN. Every other operation will still be +TfLite will then delegate operations, that can be accelerated with Arm NN, to Arm NN. Every other operation will still be 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). |