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mirror of https://github.com/vee1e/tflite-micro - Infrastructure to enable deployment of ML models to low-powe
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- 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
CI Status
| Group | Status |
|---|---|
| Core |    |
| Targets |    •  |
| Misc |  |
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 |  |
| Ingenic MIPS 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 |  |
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
- Compression
- Python Dev Guide
- Automatically Generated Files
- Python Interpreter Guide