112 lines
6.8 KiB
Markdown
112 lines
6.8 KiB
Markdown
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* [TensorFlow Lite for Microcontrollers](#tensorflow-lite-for-microcontrollers)
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* [Build Status](#build-status)
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* [Official Builds](#official-builds)
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* [Community Supported TFLM Examples](#community-supported-tflm-examples)
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* [Community Supported Kernels and Unit Tests](#community-supported-kernels-and-unit-tests)
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* [Contributing](#contributing)
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* [Getting Help](#getting-help)
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* [Additional Documentation](#additional-documentation)
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* [RFCs](#rfcs)
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<!-- Added by: advaitjain, at: Mon 04 Oct 2021 11:23:57 AM PDT -->
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<!--te-->
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# TensorFlow Lite for Microcontrollers
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TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to
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run machine learning models on DSPs, microcontrollers and other devices with
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limited memory.
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Additional Links:
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* [Tensorflow github repository](https://github.com/tensorflow/tensorflow/)
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* [TFLM at tensorflow.org](https://www.tensorflow.org/lite/microcontrollers)
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# Build Status
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* [GitHub Status](https://www.githubstatus.com/)
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## Official Builds
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Build Type | Status |
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----------- | --------------|
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CI (Linux) | [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_ci.yml) |
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Code Sync | [](https://github.com/tensorflow/tflite-micro/actions/workflows/sync.yml) |
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## Community Supported TFLM Examples
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This table captures platforms that TFLM has been ported to. Please see
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[New Platform Support](tensorflow/lite/micro/docs/new_platform_support.md) for
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additional documentation.
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Platform | Status |
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----------- | --------------|
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Arduino | [](https://github.com/tensorflow/tflite-micro-arduino-examples/actions/workflows/ci.yml) [](https://github.com/antmicro/tensorflow-arduino-examples/actions/workflows/test_examples.yml) |
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[Coral Dev Board Micro](https://coral.ai/products/dev-board-micro) | [TFLM + EdgeTPU Examples for Coral Dev Board Micro](https://github.com/google-coral/coralmicro) |
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Espressif Systems Dev Boards | [](https://github.com/espressif/tflite-micro-esp-examples/actions/workflows/ci.yml) |
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Renesas Boards | [TFLM Examples for Renesas Boards](https://github.com/renesas/tflite-micro-renesas) |
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Silicon Labs Dev Kits | [TFLM Examples for Silicon Labs Dev Kits](https://github.com/SiliconLabs/tflite-micro-efr32-examples)
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Sparkfun Edge | [](https://github.com/advaitjain/tflite-micro-sparkfun-edge-examples/actions/workflows/ci.yml)
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Texas Instruments Dev Boards | [](https://github.com/TexasInstruments/tensorflow-lite-micro-examples/actions/workflows/ci.yml)
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## Community Supported Kernels and Unit Tests
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This is a list of targets that have optimized kernel implementations and/or run
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the TFLM unit tests using software emulation or instruction set simulators.
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Build Type | Status |
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----------- | --------------|
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Cortex-M | [](https://github.com/tensorflow/tflite-micro/actions/workflows/cortex_m.yml) |
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Hexagon | [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_hexagon.yml) |
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RISC-V | [](https://github.com/tensorflow/tflite-micro/actions/workflows/riscv.yml) |
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Xtensa | [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_xtensa.yml) |
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Generate Integration Test | [](https://github.com/tensorflow/tflite-micro/actions/workflows/generate_integration_tests.yml) |
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# Contributing
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See our [contribution documentation](CONTRIBUTING.md).
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# Getting Help
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A [Github issue](https://github.com/tensorflow/tflite-micro/issues/new/choose)
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should be the primary method of getting in touch with the TensorFlow Lite Micro
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(TFLM) team.
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The following resources may also be useful:
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1. SIG Micro [email group](https://groups.google.com/a/tensorflow.org/g/micro)
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and
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[monthly meetings](http://doc/1YHq9rmhrOUdcZnrEnVCWvd87s2wQbq4z17HbeRl-DBc).
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1. SIG Micro [gitter chat room](https://gitter.im/tensorflow/sig-micro).
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1. For questions that are not specific to TFLM, please consult the broader TensorFlow project, e.g.:
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* Create a topic on the [TensorFlow Discourse forum](https://discuss.tensorflow.org)
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* Send an email to the [TensorFlow Lite mailing list](https://groups.google.com/a/tensorflow.org/g/tflite)
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* Create a [TensorFlow issue](https://github.com/tensorflow/tensorflow/issues/new/choose)
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* Create a [Model Optimization Toolkit](https://github.com/tensorflow/model-optimization) issue
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# Additional Documentation
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* [Continuous Integration](docs/continuous_integration.md)
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* [Benchmarks](tensorflow/lite/micro/benchmarks/README.md)
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* [Profiling](tensorflow/lite/micro/docs/profiling.md)
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* [Memory Management](tensorflow/lite/micro/docs/memory_management.md)
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* [Logging](tensorflow/lite/micro/docs/logging.md)
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* [Porting Reference Kernels from TfLite to TFLM](tensorflow/lite/micro/docs/porting_reference_ops.md)
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* [Optimized Kernel Implementations](tensorflow/lite/micro/docs/optimized_kernel_implementations.md)
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* [New Platform Support](tensorflow/lite/micro/docs/new_platform_support.md)
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* Platform/IP support
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* [Arm IP support](tensorflow/lite/micro/docs/arm.md)
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* [Software Emulation with Renode](tensorflow/lite/micro/docs/renode.md)
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* [Software Emulation with QEMU](tensorflow/lite/micro/docs/qemu.md)
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* [Python Dev Guide](docs/python.md)
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* [Automatically Generated Files](docs/automatically_generated_files.md)
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* [Python Interpreter Guide](python/tflite_micro/README.md)
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# RFCs
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1. [Pre-allocated tensors](tensorflow/lite/micro/docs/rfc/001_preallocated_tensors.md)
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1. [TensorFlow Lite for Microcontrollers Port of 16x8 Quantized Operators](tensorflow/lite/micro/docs/rfc/002_16x8_quantization_port.md)
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