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) * [Official Builds](#official-builds) * [Community Supported TFLM Examples](#community-supported-tflm-examples) * [Community Supported Kernels and Unit Tests](#community-supported-kernels-and-unit-tests) * [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 * [GitHub Status](https://www.githubstatus.com/) ## Official Builds Build Type | Status | ----------- | --------------| CI (Linux) | [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_ci.yml) | Code Sync | [](https://github.com/tensorflow/tflite-micro/actions/workflows/sync.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) | 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) ## Community Supported Kernels and Unit Tests 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 | [](https://github.com/tensorflow/tflite-micro/actions/workflows/cortex_m.yml) | Hexagon | [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_hexagon.yml) | RISC-V | [](https://github.com/tensorflow/tflite-micro/actions/workflows/riscv.yml) | Xtensa | [](https://github.com/tensorflow/tflite-micro/actions/workflows/run_xtensa.yml) | Generate Integration Test | [](https://github.com/tensorflow/tflite-micro/actions/workflows/generate_integration_tests.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) * [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: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"