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tensorflow/lite/g3doc/microcontrollers/index.md

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+# TensorFlow Lite for Microcontrollers
+
+TensorFlow Lite for Microcontrollers is designed to run machine learning models
+on microcontrollers and other devices with only a few kilobytes of memory. The
+core runtime just fits in 16 KB on an Arm Cortex M3 and can run many basic
+models. It doesn't require operating system support, any standard C or C++
+libraries, or dynamic memory allocation.
+
+Note: The
+[TensorFlow Lite 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.
+
+## Why microcontrollers are important
+
+Microcontrollers are typically small, low-powered computing devices that are
+embedded within hardware that requires basic computation. By bringing machine
+learning to tiny microcontrollers, we can boost the intelligence of billions of
+devices that we use in our lives, including household appliances and Internet of
+Things devices, without relying on expensive hardware or reliable internet
+connections, which is often subject to bandwidth and power constraints and
+results in high latency. This can also help preserve privacy, since no data
+leaves the device. Imagine smart appliances that can adapt to your daily
+routine, intelligent industrial sensors that understand the difference between
+problems and normal operation, and magical toys that can help kids learn in fun
+and delightful ways.
+
+## Supported platforms
+
+TensorFlow Lite for Microcontrollers is written in C++ 17 and requires a 32-bit
+platform. It has been tested extensively with many processors based on the
+[Arm Cortex-M Series](https://developer.arm.com/ip-products/processors/cortex-m)
+architecture, and has been ported to other architectures including
+[ESP32](https://www.espressif.com/en/products/hardware/esp32/overview). The
+framework is available as an Arduino library. It can also generate projects for
+development environments such as Mbed. It is open source and can be included in
+any C++ 17 project.
+
+The following development boards are supported:
+
+*   [Arduino Nano 33 BLE Sense](https://store-usa.arduino.cc/products/arduino-nano-33-ble-sense-with-headers)
+*   [SparkFun Edge](https://www.sparkfun.com/products/15170)
+*   [STM32F746 Discovery kit](https://www.st.com/en/evaluation-tools/32f746gdiscovery.html)
+*   [Adafruit EdgeBadge](https://www.adafruit.com/product/4400)
+*   [Adafruit TensorFlow Lite for Microcontrollers Kit](https://www.adafruit.com/product/4317)
+*   [Adafruit Circuit Playground Bluefruit](https://learn.adafruit.com/tensorflow-lite-for-circuit-playground-bluefruit-quickstart?view=all)
+*   [Espressif ESP32-DevKitC](https://www.espressif.com/en/products/hardware/esp32-devkitc/overview)
+*   [Espressif ESP-EYE](https://www.espressif.com/en/products/hardware/esp-eye/overview)
+*   [Wio Terminal: ATSAMD51](https://www.seeedstudio.com/Wio-Terminal-p-4509.html)
+*   [Himax WE-I Plus EVB Endpoint AI Development Board](https://www.sparkfun.com/products/17256)
+*   [Synopsys DesignWare ARC EM Software Development Platform](https://www.synopsys.com/dw/ipdir.php?ds=arc-em-software-development-platform)
+*   [Sony Spresense](https://developer.sony.com/develop/spresense/)
+
+## Explore the examples
+
+Each example application is on
+[Github](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/examples)
+and has a `README.md` file that explains how it can be deployed to its supported
+platforms. Some examples also have end-to-end tutorials using a specific
+platform, as given below:
+
+*   [Hello World](https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/examples/hello_world) -
+    Demonstrates the absolute basics of using TensorFlow Lite for
+    Microcontrollers
+    *   [Tutorial using any supported device](get_started_low_level.md)
+*   [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"
+    *   [Tutorial using SparkFun Edge](https://codelabs.developers.google.com/codelabs/sparkfun-tensorflow/#0)
+*   [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
+
+## Workflow
+
+The following steps are required to deploy and run a TensorFlow model on a
+microcontroller:
+
+1.  **Train a model**:
+    *   *Generate a small TensorFlow model* that can fit your target device and
+        contains [supported operations](build_convert.md#operation-support).
+    *   *Convert to a TensorFlow Lite model* using the
+        [TensorFlow Lite converter](build_convert.md#model-conversion).
+    *   *Convert to a C byte array* using
+        [standard tools](build_convert.md#convert-to-a-c-array) to store it in a
+        read-only program memory on device.
+2.  **Run inference** on device using the [C++ library](library.md) and process
+    the results.
+
+## Limitations
+
+TensorFlow Lite for Microcontrollers is designed for the specific constraints of
+microcontroller development. If you are working on more powerful devices (for
+example, an embedded Linux device like the Raspberry Pi), the standard
+TensorFlow Lite framework might be easier to integrate.
+
+The following limitations should be considered:
+
+*   Support for a [limited subset](build_convert.md#operation-support) of
+    TensorFlow operations
+*   Support for a limited set of devices
+*   Low-level C++ API requiring manual memory management
+*   On device training is not supported
+
+## Next steps
+
+*   [Get started with microcontrollers](get_started_low_level.md) to try the
+    example application and learn how to use the API.
+*   [Understand the C++ library](library.md) to learn how to use the library in
+    your own project.
+*   [Build and convert models](build_convert.md) to learn more about training
+    and converting models for deployment on microcontrollers.