| commit | 50433190cf493d5b9a0dd729a05975245dfc8433 | [log] [tgz] |
|---|---|---|
| author | David Davis <ddavis-2015@users.noreply.github.com> | Tue Nov 28 09:43:15 2023 -0800 |
| committer | GitHub <noreply@github.com> | Tue Nov 28 17:43:15 2023 +0000 |
| tree | 4f5ff4cd2fbf713576b47bfb5eaf3869864568a2 | |
| parent | 944b23edbffb3fda1ad74b55b9cff2480d9368b7 [diff] |
SPACE_TO_BATCH_ND: update output tensor shape (#2335) @tensorflow/micro Update the output tensor shape during prepare phase when the computed shape does not match the shape in the flatbuffer. Added additional tests from TfLite. See #2319 for additional details. Resolves [b/313360569](https://issuetracker.google.com/313360569) bug=fixes #2332 fixes #2319 #1646 #1629 #1231
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.: