To package the module `runtime` as `tflite_micro.runtime`, put `runtime` under a directory representing the Python namespace package `tflite_micro`. For organization's sake, move it all to the top-level directory `python/`. Adjust tests and docs to match. Some code outside of the Python extension module has come to depend on `python/tflite_micro:python_ops_resolver` as a replacement for `all_ops_resolver` (e.g.:`t/l/m/integration_tests/seanet/add/integration_tests.cc`). `python_ops_resolver` is intended to be a private implementation detail of the Python extension module. For now, grandfather in the dependent code by updating its references to the resolver's location; however, soon the dependent code should be migrated away to a different resolver. (#2033, https://issuetracker.google.com/286508251) BUG=part of #1484
6.8 KiB
- TensorFlow Lite for Microcontrollers
- Build Status
- Contributing
- Getting Help
- Additional Documentation
- RFCs
TensorFlow Lite for Microcontrollers
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 Status
Official Builds
| Build Type | Status |
|---|---|
| CI (Linux) | |
| Code Sync |
Community Supported TFLM Examples
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 |
Community Supported Kernels and Unit Tests
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 |
Contributing
See our contribution documentation.
Getting Help
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.:
- Create a topic on the TensorFlow Discourse forum
- Send an email to the TensorFlow Lite mailing list
- Create a TensorFlow issue
- Create a Model Optimization Toolkit issue
Additional Documentation
- Continuous Integration
- Benchmarks
- Profiling
- Memory Management
- Logging
- Porting Reference Kernels from TfLite to TFLM
- Optimized Kernel Implementations
- New Platform Support
- Platform/IP support
- Software Emulation with Renode
- Software Emulation with QEMU
- Python Dev Guide
- Automatically Generated Files
- Python Interpreter Guide