commit | 213eb8769b919f722f85877a2affeddd76f45dca | [log] [tgz] |
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author | Ibrahim Abdelkader <i.abdalkader@gmail.com> | Wed Jul 24 23:53:31 2024 +0200 |
committer | GitHub <noreply@github.com> | Wed Jul 24 21:53:31 2024 +0000 |
tree | d55275ac7d3c22b16f1806b1f4c0d808e08516e3 | |
parent | d619ad8ec501a654d5893bd5bdb66f1c07f5d16a [diff] |
Update Ethos-U driver. (#2626) * Update Ethos-U driver to the latest (v24.05) * Allow passing extra C flags to the driver from the command line. * Fixes #2619 Note I tested with `TARGET=cortex_m_corstone_300 test_network_tester_test` and on real hardware as well built with: ```bash make -j12 -f tensorflow/lite/micro/tools/make/Makefile \ TARGET=cortex_m_generic TARGET_ARCH=cortex-m55 \ CO_PROCESSOR=ethos_u ETHOSU_ARCH=u55 OPTIMIZED_KERNEL_DIR=ethos_u \ CORE_OPTIMIZATION_LEVEL=-O2 KERNEL_OPTIMIZATION_LEVEL=-O2 \ THIRD_PARTY_KERNEL_OPTIMIZATION_LEVEL=-O2 \ TARGET_TOOLCHAIN_ROOT=/opt/arm-none-eabi/bin/ \ TARGET_TOOLCHAIN_PREFIX=arm-none-eabi- BUILD_TYPE=release microlite ``` BUG=#2619
TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on DSPs, microcontrollers and other devices with limited memory.
Additional Links:
Build Type | Status |
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CI (Linux) | |
Code Sync |
This table captures platforms that TFLM has been ported to. Please see New Platform Support for additional documentation.
Platform | Status |
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Arduino | |
Coral Dev Board Micro | TFLM + EdgeTPU Examples for Coral Dev Board Micro |
Espressif Systems Dev Boards | |
Renesas Boards | TFLM Examples for Renesas Boards |
Silicon Labs Dev Kits | TFLM Examples for Silicon Labs Dev Kits |
Sparkfun Edge | |
Texas Instruments Dev Boards |
This is a list of targets that have optimized kernel implementations and/or run the TFLM unit tests using software emulation or instruction set simulators.
Build Type | Status |
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Cortex-M | |
Hexagon | |
RISC-V | |
Xtensa | |
Generate Integration Test |
See our contribution documentation.
A Github issue should be the primary method of getting in touch with the TensorFlow Lite Micro (TFLM) team.
The following resources may also be useful:
SIG Micro email group and monthly meetings.
SIG Micro gitter chat room.
For questions that are not specific to TFLM, please consult the broader TensorFlow project, e.g.: