• 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 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

• GitHub Status

Build Type	Status
CI (Linux)
Code Sync

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

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

See our contribution documentation.

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 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

• Continuous Integration
• Benchmarks
• Profiling
• Memory Management
• Logging
• Porting Reference Kernels from TfLite to TFLM
• Optimized Kernel Implementations
• New Platform Support
• Platform/IP support
  • Arm IP support
• Software Emulation with Renode
• Software Emulation with QEMU
• Python Dev Guide
• Automatically Generated Files
• Python Interpreter Guide

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