| commit | d3088479753fb0963dd72d75bddbc5ff5bf64aec | [log] [tgz] |
|---|---|---|
| author | Måns Nilsson <mans.nilsson@arm.com> | Fri Mar 15 23:29:09 2024 +0100 |
| committer | GitHub <noreply@github.com> | Fri Mar 15 22:29:09 2024 +0000 |
| tree | 545f810c93ad316b3e6ea29381b0e4483faf5087 | |
| parent | f9044b03343d282e9f7dc8db489e6f02dc36278c [diff] |
Update examples and documentation for Arm(R) Corstone(TM)-300 FVP (#2503) * Updates benchmark documentation. * Updates benchmarks and network tester example. * Select only needed ops for memory measurements. * Only pip install if needed. * Also gen folder output will be created differently depending on also toolchain and type of kernels BUG=documentation for the benchmark application is not correct
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.: