- 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 |   |
| ESP32 |  |
| 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 |   |
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 inference with TFLM (for example model conversion and quantization) please use the following resources:
- Send an email to the TfLite Mailing List
- Create a TensorFlow Lite Converter Issue
- Create an issue in the model optimization toolkit GitHub repository
Additional Documentation
- Continuous Integration
- Benchmarks
- Profiling
- Memory Management
- Porting Reference Kernels from TfLite to TFLM
- Optimized Kernel Implementations
- New Platform Support
- Platform/IP support
- Software Emulation with Renode
- Python Dev Guide
- Automatically Generated Files