- 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 |
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