| commit | b766947f5a0b8f34dbe1a12b904c99d524b291d3 | [log] [tgz] |
|---|---|---|
| author | RJ Ascani <rjascani@google.com> | Wed Dec 20 16:57:49 2023 -0800 |
| committer | GitHub <noreply@github.com> | Thu Dec 21 00:57:49 2023 +0000 |
| tree | d7d71a68983fcf2e70b43e732e0e53ef9d70491d | |
| parent | 436b6d20ad88c1f5e28cecade60ae85ddde7ecdf [diff] |
codegen: Add Bazel macro for inference library (#2366) The TFLM code_generator takes a TFLite model and generates C/C++ source code that can then be compiled into a binary. This change adds a Bazel macro for invoking the code generator and creating a cc_library with the resulting sources. It also updates the checked-in hello_world example with an appropriate BUILD file and updates the script to use Bazel instead of make. BUG=b/294230402
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 |
|---|---|
| CI (Linux) |  |
| Code Sync |  |
This table captures platforms that TFLM has been ported to. Please see New Platform Support for additional documentation.
| Platform | Status |
|---|---|
| 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 |
|---|---|
| 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.: