76 lines
4.1 KiB
Markdown
76 lines
4.1 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 Builds](#community-supported-builds)
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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: Thu 17 Jun 2021 09:33:15 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/ci.yml?query=event%3Aschedule) |
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Code Sync | [](https://github.com/tensorflow/tflite-micro/actions/workflows/sync.yml) |
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## Community Supported Builds
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Build Type | Status |
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----------- | --------------|
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Arduino | [](https://github.com/tensorflow/tflite-micro/actions/workflows/arduino.yml) [](https://github.com/antmicro/tensorflow-arduino-examples/actions/workflows/test_examples.yml) |
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Cortex-M | [](https://github.com/tensorflow/tflite-micro/actions/workflows/cortex_m.yml) |
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Sparkfun Edge | [](https://github.com/tensorflow/tflite-micro/actions/workflows/sparkfun_edge.yml) |
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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) |
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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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# 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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* [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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* [Software Emulation with Renode](tensorflow/lite/micro/docs/renode.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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