| commit | 7dbd9d23734c8c36ddf8e15b1d1c8a3346f5264c | [log] [tgz] |
|---|---|---|
| author | David Davis <ddavis-2015@users.noreply.github.com> | Tue May 14 10:29:11 2024 -0700 |
| committer | GitHub <noreply@github.com> | Tue May 14 17:29:11 2024 +0000 |
| tree | c78c03ce5b3d25d0c1ef68c0c41bc8ea01802fdf | |
| parent | c377b745c25a6384bab4f2ba175cdae34b1103fb [diff] |
Add meta-data to generic benchmark (#2496)
@tensorflow/micro
Add meta-data strings to the binary. Meta-data is output each time binary is executed. See the README for a sample of the output.
If a Git repo. is not available at ```${TENSORFLOW_ROOT}```, an appropriate message is generated in the meta-data.
Changes to how the CMSISNN and XTENSA NN library download scripts work.
Adds run of generic benchmark with embedded model for default build of x86, Corstone-300, Xtensa to test scripts executed during CI.
bug=fixes #2495TensorFlow 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.: