base on Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors). <!--ts--> - [TensorFlow Lite for Microcontrollers](#tensorflow-lite-for-microcontrollers) - [Build Status](#build-status) - [CI Status](#ci-status) - [Community Supported TFLM Examples](#community-supported-tflm-examples) - [Contributing](#contributing) - [Getting Help](#getting-help) - [Additional Documentation](#additional-documentation) - [RFCs](#rfcs) <!-- Added by: advaitjain, at: Mon 04 Oct 2021 11:23:57 AM PDT --> <!--te--> # 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 ## CI Status | Group | Status | | :--- | :--- | | Core | [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_core.yml) [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_windows.yml) [](https://github.com/tensorflow/tflite-micro/actions/workflows/sync.yml) | | Targets | [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_cortex_m.yml) [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_riscv.yml) [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_hexagon.yml) [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_xtensa.yml) | | Misc | [](https://github.com/tensorflow/tflite-micro/actions/workflows/generate_integration_tests.yml) | ## Community Supported TFLM Examples This table captures platforms that TFLM has been ported to. Please see [New Platform Support](tensorflow/lite/micro/docs/new_platform_support.md) for additional documentation. Platform | Status | ----------- | --------------| 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) | [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) | Espressif Systems Dev Boards | [](https://github.com/espressif/tflite-micro-esp-examples/actions/workflows/ci.yml) | Ingenic MIPS Boards | [](https://github.com/yinzara/ingenic-tflite-micro/tree/main/examples/hello_world) | Renesas Boards | [TFLM Examples for Renesas Boards](https://github.com/renesas/tflite-micro-renesas) | Silicon Labs Dev Kits | [TFLM Examples for Silicon Labs Dev Kits](https://github.com/SiliconLabs/tflite-micro-efr32-examples) Sparkfun Edge | [](https://github.com/advaitjain/tflite-micro-sparkfun-edge-examples/actions/workflows/ci.yml) Texas Instruments Dev Boards | [](https://github.com/TexasInstruments/tensorflow-lite-micro-examples/actions/workflows/ci.yml) # 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 TFLM, please consult the broader TensorFlow project, e.g.: * Create a topic on the [TensorFlow Discourse forum](https://discuss.tensorflow.org) * Send an email to the [TensorFlow Lite mailing list](https://groups.google.com/a/tensorflow.org/g/tflite) * Create a [TensorFlow issue](https://github.com/tensorflow/tensorflow/issues/new/choose) * Create a [Model Optimization Toolkit](https://github.com/tensorflow/model-optimization) issue # 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) * [Logging](tensorflow/lite/micro/docs/logging.md) * [Porting Reference Kernels from TfLite to TFLM](tensorflow/lite/micro/docs/porting_reference_ops.md) * [Optimized Kernel Implementations](tensorflow/lite/micro/docs/optimized_kernel_implementations.md) * [New Platform Support](tensorflow/lite/micro/docs/new_platform_support.md) * Platform/IP support * [Arm IP support](tensorflow/lite/micro/docs/arm.md) * [Software Emulation with Renode](tensorflow/lite/micro/docs/renode.md) * [Software Emulation with QEMU](tensorflow/lite/micro/docs/qemu.md) * [Compression](tensorflow/lite/micro/docs/compression.md) * [MNIST Compression Tutorial](tensorflow/lite/micro/compression/mnist_compression_tutorial.ipynb) * [Python Dev Guide](docs/python.md) * [Automatically Generated Files](docs/automatically_generated_files.md) * [Python Interpreter Guide](python/tflite_micro/README.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) ", Assign "at most 3 tags" to the expected json: {"id":"4226","tags":[]} "only from the tags list I provide: []" returns me the "expected json"