| commit | 06499e832f362b39835ec389e82ab37cec00a37b | [log] [tgz] |
|---|---|---|
| author | RJ Ascani <rjascani@google.com> | Tue Jan 09 11:54:12 2024 -0800 |
| committer | GitHub <noreply@github.com> | Tue Jan 09 19:54:12 2024 +0000 |
| tree | abb249289810e8470fa20e31d29955b5fa1544bb | |
| parent | a9405e323346027cf044a7f721189383f21bb892 [diff] |
codegen: Remove make helpers for codegen (#2378) This partially reverts commit 1e9b4c5d849a65f5a8b52686ab1db5ceb4e40d18. The make helpers for running the inference code generator were somewhat of a hack due to the desire to integrate a target-specific preprocessor binary as part of the process. Since that is no longer necessary, we can now switch over to just using Bazel for running the code generator. 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.: