* [TensorFlow Lite for Microcontrollers](#tensorflow-lite-for-microcontrollers) * [Build Status](#build-status) * [Official Builds](#official-builds) * [Community Supported Builds](#community-supported-builds) * [Contributing](#contributing) * [Getting Help](#getting-help) * [Additional Documentation](#additional-documentation) * [RFCs](#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](https://github.com/tensorflow/tensorflow/) * [TFLM at tensorflow.org](https://www.tensorflow.org/lite/microcontrollers) # Build Status * [GitHub Status](https://www.githubstatus.com/) ## Official Builds Build Type | Status | ----------- | --------------| CI (Linux) | [](https://github.com/tensorflow/tflite-micro/actions/workflows/ci.yml?query=event%3Aschedule) | Code Sync | [](https://github.com/tensorflow/tflite-micro/actions/workflows/sync.yml) | ## Community Supported Builds Build Type | Status | ----------- | --------------| Arduino | [](https://github.com/tensorflow/tflite-micro/actions/workflows/arduino.yml) [](https://github.com/antmicro/tensorflow-arduino-examples/actions/workflows/test_examples.yml) | Cortex-M | [](https://github.com/tensorflow/tflite-micro/actions/workflows/cortex_m.yml) | ESP32 | [](https://github.com/tensorflow/tflite-micro/actions/workflows/esp32.yml) | RISC-V | [](https://github.com/tensorflow/tflite-micro/actions/workflows/riscv.yml) | Sparkfun Edge | [](https://github.com/tensorflow/tflite-micro/actions/workflows/sparkfun_edge.yml) | Xtensa | [](https://github.com/tensorflow/tflite-micro/actions/workflows/xtensa.yml?query=event%3Aschedule) [](https://github.com/advaitjain/tflite-micro/tree/local-continuous-builds/tensorflow/lite/micro/docs/local_continuous_builds/xtensa.md#summary) | # Contributing See our [contribution documentation](CONTRIBUTING.md). # Getting Help A [Github issue](https://github.com/tensorflow/tflite-micro/issues/new/choose) 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](https://groups.google.com/a/tensorflow.org/g/micro) and [monthly meetings](http://doc/1YHq9rmhrOUdcZnrEnVCWvd87s2wQbq4z17HbeRl-DBc). 1. SIG Micro [gitter chat room](https://gitter.im/tensorflow/sig-micro). 1. 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](https://groups.google.com/a/tensorflow.org/g/tflite) * Create a [TensorFlow Lite Converter Issue](https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=TFLiteConverter&template=60-tflite-converter-issue.md) * Create an issue in the [model optimization toolkit](https://github.com/tensorflow/model-optimization) GitHub repository # Additional Documentation * [Continuous Integration](docs/continuous_integration.md) * [Benchmarks](tensorflow/lite/micro/benchmarks/README.md) * [Profiling](tensorflow/lite/micro/docs/profiling.md) * [Memory Management](tensorflow/lite/micro/docs/memory_management.md) * [Optimized Kernel Implementations](tensorflow/lite/micro/docs/optimized_kernel_implementations.md) * [New Platform Support](tensorflow/lite/micro/docs/new_platform_support.md) * [Software Emulation with Renode](tensorflow/lite/micro/docs/renode.md) # RFCs 1. [Pre-allocated tensors](tensorflow/lite/micro/docs/rfc/001_preallocated_tensors.md) 1. [TensorFlow Lite for Microcontrollers Port of 16x8 Quantized Operators](tensorflow/lite/micro/docs/rfc/002_16x8_quantization_port.md)