머신러닝 모델을 실행하도록 설계된 마이크로컨트롤러용 LiteRT
몇 킬로바이트의 메모리만 있는 마이크로컨트롤러 및 기타 장치에 사용될 수 있습니다. 이
코어 런타임은 Arm Cortex M3에서 16KB로 적합하며 많은 기본 애플리케이션을 실행할 수 있습니다.
모델을 학습시키는 작업도
반복해야 합니다 운영체제 지원, 표준 C 또는 C++ 불필요
라이브러리 또는 동적 메모리 할당이 있습니다.
마이크로컨트롤러가 중요한 이유
마이크로컨트롤러는 일반적으로 소형 저전력 컴퓨팅 장치로서
하드웨어에 내장되어 있기 때문에 기본적인 컴퓨팅이 필요합니다. 머신을
작은 마이크로컨트롤러에 대해 배우면 수십억 개의 지능을 가지고 있는
일상 생활에서 사용하는 장치의 종류에 따라
값비싼 하드웨어나 안정적인 인터넷에 의존하지 않고 사물 기기 사용
이는 종종 대역폭 및 전력 제약의 영향을 받으며
지연 시간이 길어집니다 이렇게 하면 데이터가 수집되지 않아
기기를 벗어납니다. 일상 생활에 적응하는 스마트 가전제품을 상상해 보세요.
루틴, 지능형 산업용 센서로
아이들이 재미있게 학습하는 데 도움이 될 수 있는 신비로운 장난감과
즐거운 방법을 찾는 것입니다.
지원되는 플랫폼
마이크로컨트롤러용 LiteRT는 C++ 17로 작성되며 32비트
있습니다. 운영체제에 기반한 많은 프로세서를 통해 광범위하게 테스트되었습니다.
Arm Cortex-M 시리즈
컨테이너화된 애플리케이션 아키텍처를 포함하여
ESP32 이
Arduino 라이브러리로 제공됩니다. 또한 kubectl 명령어
개발 환경(예: Mbed)을 이는 오픈소스이며
모든 C++ 17 프로젝트
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["필요한 정보가 없음","missingTheInformationINeed","thumb-down"],["너무 복잡함/단계 수가 너무 많음","tooComplicatedTooManySteps","thumb-down"],["오래됨","outOfDate","thumb-down"],["번역 문제","translationIssue","thumb-down"],["샘플/코드 문제","samplesCodeIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-07-24(UTC)"],[],[],null,["# LiteRT for Microcontrollers is designed to run machine learning models\non microcontrollers and other devices with only a few kilobytes of memory. The\ncore runtime just fits in 16 KB on an Arm Cortex M3 and can run many basic\nmodels. It doesn't require operating system support, any standard C or C++\nlibraries, or dynamic memory allocation.\n| **Note:** The [LiteRT for Microcontrollers Experiments](https://experiments.withgoogle.com/collection/tfliteformicrocontrollers) features work by developers combining Arduino and TensorFlow to create awesome experiences and tools. Check out the site for inspiration to create your own TinyML projects.\n\nWhy microcontrollers are important\n----------------------------------\n\nMicrocontrollers are typically small, low-powered computing devices that are\nembedded within hardware that requires basic computation. By bringing machine\nlearning to tiny microcontrollers, we can boost the intelligence of billions of\ndevices that we use in our lives, including household appliances and Internet of\nThings devices, without relying on expensive hardware or reliable internet\nconnections, which is often subject to bandwidth and power constraints and\nresults in high latency. This can also help preserve privacy, since no data\nleaves the device. Imagine smart appliances that can adapt to your daily\nroutine, intelligent industrial sensors that understand the difference between\nproblems and normal operation, and magical toys that can help kids learn in fun\nand delightful ways.\n\nSupported platforms\n-------------------\n\nLiteRT for Microcontrollers is written in C++ 17 and requires a 32-bit\nplatform. It has been tested extensively with many processors based on the\n[Arm Cortex-M Series](https://developer.arm.com/ip-products/processors/cortex-m)\narchitecture, and has been ported to other architectures including\n[ESP32](https://www.espressif.com/en/products/hardware/esp32/overview). The\nframework is available as an Arduino library. It can also generate projects for\ndevelopment environments such as Mbed. It is open source and can be included in\nany C++ 17 project.\n\nThe following development boards are supported:\n\n- [Arduino Nano 33 BLE Sense](https://store-usa.arduino.cc/products/arduino-nano-33-ble-sense-with-headers)\n- [SparkFun Edge](https://www.sparkfun.com/products/15170)\n- [STM32F746 Discovery kit](https://www.st.com/en/evaluation-tools/32f746gdiscovery.html)\n- [Adafruit EdgeBadge](https://www.adafruit.com/product/4400)\n- [Adafruit LiteRT for Microcontrollers Kit](https://www.adafruit.com/product/4317)\n- [Adafruit Circuit Playground Bluefruit](https://learn.adafruit.com/tensorflow-lite-for-circuit-playground-bluefruit-quickstart?view=all)\n- [Espressif ESP32-DevKitC](https://www.espressif.com/en/products/hardware/esp32-devkitc/overview)\n- [Espressif ESP-EYE](https://www.espressif.com/en/products/hardware/esp-eye/overview)\n- [Wio Terminal: ATSAMD51](https://www.seeedstudio.com/Wio-Terminal-p-4509.html)\n- [Himax WE-I Plus EVB Endpoint AI Development Board](https://www.sparkfun.com/products/17256)\n- [Synopsys DesignWare ARC EM Software Development Platform](https://www.synopsys.com/dw/ipdir.php?ds=arc-em-software-development-platform)\n- [Sony Spresense](https://developer.sony.com/develop/spresense/)\n\nExplore the examples\n--------------------\n\nEach example application is on\n[GitHub](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/examples)\nand has a `README.md` file that explains how it can be deployed to its supported\nplatforms. Some examples also have end-to-end tutorials using a specific\nplatform, as given below:\n\n- [Hello World](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/examples/hello_world) - Demonstrates the absolute basics of using LiteRT for Microcontrollers\n - [Tutorial using any supported device](./get_started)\n- [Micro speech](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/examples/micro_speech) - Captures audio with a microphone to detect the words \"yes\" and \"no\"\n - [Tutorial using SparkFun Edge](https://codelabs.developers.google.com/codelabs/sparkfun-tensorflow/#0)\n- [Person detection](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/examples/person_detection) - Captures camera data with an image sensor to detect the presence or absence of a person\n\nWorkflow\n--------\n\nThe following steps are required to deploy and run a TensorFlow model on a\nmicrocontroller:\n\n1. **Train a model** :\n - *Generate a small TensorFlow model* that can fit your target device and contains [supported operations](./build_convert#operation_support).\n - *Convert to a LiteRT model* using the [LiteRT converter](./build_convert#model_conversion).\n - *Convert to a C byte array* using [standard tools](./build_convert#convert_to_a_c_array) to store it in a read-only program memory on device.\n2. **Run inference** on device using the [C++ library](./library) and process the results.\n\nLimitations\n-----------\n\nLiteRT for Microcontrollers is designed for the specific constraints of\nmicrocontroller development. If you are working on more powerful devices (for\nexample, an embedded Linux device like the Raspberry Pi), the standard\nLiteRT framework might be easier to integrate.\n\nThe following limitations should be considered:\n\n- Support for a [limited subset](./build_convert#operation_support) of TensorFlow operations\n- Support for a limited set of devices\n- Low-level C++ API requiring manual memory management\n- On device training is not supported\n\nNext steps\n----------\n\n- [Get started with microcontrollers](./get_started) to try the example application and learn how to use the API.\n- [Understand the C++ library](./library) to learn how to use the library in your own project.\n- [Build and convert models](./build_convert) to learn more about training and converting models for deployment on microcontrollers."]]
