nyadla-sys / tflite-micro

TensorFlow Lite for Microcontrollers

• TensorFlow Lite for Microcontrollers
• Build Status
  • Official Builds
  • Community Supported TFLM Examples
  • Community Supported Kernels and Unit Tests
• 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:

• Tensorflow github repository
• TFLM at tensorflow.org

Build Status

• GitHub 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
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:

1. SIG Micro email group and monthly meetings.

2. SIG Micro gitter chat room.

3. 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
• Software Emulation with Renode
• Python Dev Guide
• Automatically Generated Files

RFCs

1. Pre-allocated tensors
2. TensorFlow Lite for Microcontrollers Port of 16x8 Quantized Operators