tensorflow-1.14.0.zip

# TensorFlow Lite for Microcontrollers This an experimental port of TensorFlow Lite aimed at micro controllers and other devices with only kilobytes of memory. It doesn't require any operating system support, any standard C or C++ libraries, or dynamic memory allocation, so it's designed to be portable even to 'bare metal' systems. The core runtime fits in 16KB on a Cortex M3, and with enough operators to run a speech keyword detection model, takes up a total of 22KB. ## Table of Contents - [Getting Started](#getting-started) * [Getting Started with Portable Reference Code](#getting-started-with-portable-reference-code) * [Building Portable Reference Code using Make](#building-portable-reference-code-using-make) * [Building for the "Blue Pill" STM32F103 using Make](#building-for-the-blue-pill-stm32f103-using-make) * [Building for "Hifive1" SiFive FE310 development board using Make](#building-for-hifive1-sifive-fe310-development-board-using-make) * [Building for Ambiq Micro Apollo3Blue EVB using Make](#building-for-ambiq-micro-apollo3blue-evb-using-make) * [Additional Apollo3 Instructions](#additional-apollo3-instructions) * [Building for the Eta Compute ECM3531 EVB using Make](#Building-for-the-Eta-Compute-ECM3531-EVB-using-Make) - [Goals](#goals) - [Generating Project Files](#generating-project-files) - [Generating Arduino Libraries](#generating-arduino_libraries) - [How to Port TensorFlow Lite Micro to a New Platform](#how-to-port-tensorflow-lite-micro-to-a-new-platform) * [Requirements](#requirements) * [Getting Started](getting-started) * [Troubleshooting](#troubleshooting) * [Optimizing for your Platform](#optimizing-for-your-platform) * [Code Module Organization](#code-module-organization) * [Working with Generated Projects](#working-with-generated-projects) * [Supporting a Platform with Makefiles](#supporting-a-platform-with-makefiles) * [Supporting a Platform with Emulation Testing](#supporting-a-platform-with-emulation-testing) * [Implementing More Optimizations](#implementing-more-optimizations) # Getting Started One of the challenges of embedded software development is that there are a lot of different architectures, devices, operating systems, and build systems. We aim to support as many of the popular combinations as we can, and make it as easy as possible to add support for others. If you're a product developer, we have build instructions or pre-generated project files that you can download for the following platforms: Device | Mbed | Keil | Make/GCC ---------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------ | -------- [STM32F746G Discovery Board](https://www.st.com/en/evaluation-tools/32f746gdiscovery.html) | [Download](https://drive.google.com/open?id=1OtgVkytQBrEYIpJPsE8F6GUKHPBS3Xeb) | - | [Download](https://drive.google.com/open?id=1u46mTtAMZ7Y1aD-He1u3R8AE4ZyEpnOl) ["Blue Pill" STM32F103-compatible development board](https://github.com/google/stm32_bare_lib) | - | - | [Instructions](#building-for-the-blue-pill-stm32f103-using-make) [Ambiq Micro Apollo3Blue EVB using Make](https://ambiqmicro.com/apollo-ultra-low-power-mcus/) | - | - | [Instructions](#building-for-ambiq-micro-apollo3blue-evb-using-make) [Generic Keil uVision Projects](http://www2.keil.com/mdk5/uvision/) | - | [Download](https://drive.google.com/open?id=1Lw9rsdquNKObozClLPoE5CTJLuhfh5mV) | - [Eta Compute ECM3531 EVB](https://etacompute.com/) | - | - | [Instructions](#Building-for-the-Eta-Compute-ECM3531-EVB-using-Make) If your device is not yet supported, it may not be too hard to add support. You can learn about that process [here](#how-to-port-tensorflow-lite-micro-to-a-new-platform). We're looking forward to getting your help expanding this table! ## Getting Started with Portable Reference Code If you don't have a particular microcontroller platform in mind yet, or just want to try out the code before beginning porting, the easiest way to begin is by [downloading the platform-agnostic reference code](https://drive.google.com/open?id=1cawEQAkqquK_SO4crReDYqf_v7yAwOY8). You'll see a series of folders inside the archive, with each one containing just the source files you need to build one binary. There is a simple Makefile for each folder, but you should be able to load the files into almost any IDE and build them. There's also a [Visual Studio Code](https://code.visualstudio.com/) project file already set up, so you can easily explore the code in a cross-platform IDE. ## Building Portable Reference Code using Make It's easy to build portable reference code directly from GitHub using make if you're on a Linux or OS X machine with an internet connection. - Open a terminal - Download the TensorFlow source with `git clone https://github.com/tensorflow/tensorflow.git` - Enter the source root directory by running `cd tensorflow` - Build and test the library with `make -f tensorflow/lite/experimental/micro/tools/make/Makefile test` You should see a series of compilation steps, followed by `~~~ALL TESTS PASSED~~~` for the various tests of the code that it will run. If there's an error, you should get an informative message from make about what went wrong. These tests are all built as simple binaries with few dependencies, so you can run them manually. For example, here's how to run the depthwise convolution test, and its output: ``` tensorflow/lite/experimental/micro/tools/make/gen/linux_x86_64/bin/tensorflow/lite/experimental/micro/kernels/depthwise_conv_test Testing SimpleTest Testing SimpleTestQuantized Testing SimpleTestRelu Testing SimpleTestReluQuantized 4/4 tests passed ~ALL TESTS PASSED~~~ ``` Looking at the [depthwise_conv_test.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/kernels/depthwise_conv_test.cc) code, you'll see a sequence that looks like this: ``` ... TF_LITE_MICRO_TESTS_BEGIN TF_LITE_MICRO_TEST(SimpleTest) { ... } ... TF_LITE_MICRO_TESTS_END ``` These macros work a lot like [the Google test framework](https://github.com/google/googletest), but they don't require any dependencies and just write results to stderr, rather than aborting the program. If all the tests pass, then `~~~ALL TESTS PASSED~~~` is output, and the test harness that runs the binary during the make process knows that everything ran correctly. If there's an error, the lack of the expected string lets the harness know that the test failed. So, why are we running tests in this complicated way? So far, we've been building binaries that run locally on the Mac OS or Linux machine you're building on, but this approach becomes important when we're targeting simple micro controller devices. ## Building for the "Blue Pill" STM32F103 using Make The goal of this library is to enable machine learn