TinyML: Getting Started with TensorFlow Lite for Microcontrollers | Digi-Key Electronics

Publicado em: 06 Julho 2020
no canal de: DigiKey

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In this tutorial, Shawn shows you how to use the TensorFlow Lite for Microcontrollers library to perform machine learning tasks on embedded systems. Specifically, he uses the STM32CubeIDE, but TensorFlow Lite for Microcontrollers can be copied to almost any embedded build system.

You will need first need to train a sample neural network by following the steps in this video:    • Intro to TinyML Part 1: Training a Neural ...  . Download all three model files (.h5, .tflite, .h).

We show you how to generate the TensorFlow Lite for Microcontrollers source code files using the Make build system. Note that for this step, you will need access to Linux or macOS. From there, you can copy the model file and TensorFlow Lite source code files to your embedded project directory.

We demonstrate how to include the necessary TensorFlow Lite source files and any changes that need to be made to them. After, we walk you through the code for running inference using the trained neural network.

Finally, we measure the required flash and RAM used to run our basic neural network as well as the time it takes to run inference. These numbers can be used to compare against other machine learning frameworks, such as X-Cube-AI.

Before starting, we recommend you watch the following videos:
What is Edge AI    • Intro to Edge AI: Machine Learning + IoT –...
Getting Started with Machine Learning Using TensorFlow and Keras    • Getting Started with TensorFlow and Keras ...
Intro to TinyML Part 1: Training a Neural Network for Arduino in TensorFlow    • Intro to TinyML Part 1: Training a Neural ...

Product Links:
Nucleo-L432KC - https://www.digikey.com/product-detai...

Related Videos:
Intro to Edge AI
   • Intro to Edge AI: Machine Learning + IoT –...

Getting Started with Machine Learning Using TensorFlow and Keras
   • Getting Started with TensorFlow and Keras ...

Intro to TensorFlow Lite Part 1: Wake Word Feature Extraction
   • Intro to TensorFlow Lite Part 1: Wake Word...

Intro to TensorFlow Lite Part 2: Speech Recognition Model Training
   • Intro to TensorFlow Lite Part 2: Speech Re...

Intro to TensorFlow Lite Part 3: Speech Recognition on Raspberry Pi
   • Intro to TensorFlow Lite Part 3: Speech Re...

Low-Cost Data Acquisition (DAQ) with Arduino and Binho for Machine Learning
   • Low-Cost Data Acquisition (DAQ) with Ardui...

Intro to TinyML Part 1: Training a Neural Network for Arduino in TensorFlow
   • Intro to TinyML Part 1: Training a Neural ...

Intro to TinyML Part 2: Deploying a TensorFlow Lite Model to Arduino
   • Intro to TinyML Part  2: Deploying a Tenso...

Edge AI Anomaly Detection Part 1: Data Collection
   • Edge AI Anomaly Detection Part 1: Data Col...

Edge AI Anomaly Detection Part 2: Feature Extraction and Model Training
   • Edge AI Anomaly Detection Part 2: Feature ...

Edge AI Anomaly Detection Part 3: Deploy Machine Learning Models to Raspberry Pi | Digi-Key
   • Edge AI Anomaly Detection Part 3: Deploy M...

Edge AI Anomaly Detection Part 4: Deploy TinyML Model in Arduino to ESP32
   • Edge AI Anomaly Detection Part  4: Deploy ...

Related Project Links:
TinyML: Getting Started with TensorFlow Lite for Microcontrollers https://www.digikey.com/en/maker/proj...

Related Articles:
What is Edge AI?
https://www.digikey.com/en/maker/proj...

Getting Started with Machine Learning Using TensorFlow and Keras
https://www.digikey.com/en/maker/proj...

TensorFlow Lite Tutorials: https://www.digikey.com/en/maker/sear...

Low-Cost Data Acquisition (DAQ) with Arduino and Binho for ML
https://www.digikey.com/en/maker/proj...

Intro to TinyML: https://www.digikey.com/en/maker/sear...

Edge AI Anomaly Detection: https://www.digikey.com/en/maker/sear...

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