tflite: mtk_neuron: add neuron delegate self abortion test This is the initial CL of testing the neuron delegate preemption/scheduling behavior. This CL includes: * A general model structure that we could control its depth, width, and inference speed. * A test to ensure the model will be aborted when inference_abort_time_ms is given. BUG=b:374245241 TEST=neuron_delegate_test on navi Change-Id: Ibeec61b3c5b88e1af8f2c64f8bafd9573079a241 Reviewed-on: https://chromium-review.googlesource.com/c/chromiumos/platform/tflite/+/6176631 Tested-by: Tommy Chiang <ototot@google.com> Reviewed-by: Shik Chen <shik@chromium.org> Commit-Queue: Tommy Chiang <ototot@google.com>
This repository hosts the core ChromeOS TFLite components, enabling on-device machine learning (ODML) workloads accelerated by NPU.
The corresponding ebuild can be found at: tensorflow-9999.ebuild
Patches are stored in the patch/ directory and explicitly listed in WORKSPACE.bazel. A helper script, ./script/patcher.py, is included to facilitate patch management within a TFLite workspace.
The typical workflow:
Eject (Download) TensorFlow Source Code
Download the TensorFlow source code into a local git repository with patches applied as individual commits:
./script/patcher.py eject
This creates a new local git repository at tensorflow/.
Modify the TensorFlow Repository
Make changes to the tensorflow/ repository as needed, following standard git workflows. Optionally, include a PATCH_NAME= tag in commit messages to specify the filename of the corresponding patch.
Seal the Repository
Regenerate the patch files and update the WORKSPACE.bazel file:
./script/patcher.py seal
This updates the patches in the patch/ directory and reflects the changes in WORKSPACE.bazel.
It's preferred to submit changes to upstream TensorFlow first and cherry-pick them as patches. This helps minimize divergence and makes TensorFlow updates easier.