Introduction to tinyML

I was given a presentation on the Introduction to tinyML (Machine learning on edge) and a bit about TensorFlow lite for microcontrollers at Mozilla firefox club VIT webinar.

Transcript

1. Introduction to tinyML -- uday kiran
2. What is tinyML? • Machine learning + embedded systems = Intelligent IOT devices • It helps to explore what type of models you can run on small, low-power devices like microcontrollers. • Summarizing and analyzing data at the edge on low power devices
3. Why tinyML? • Instant response • It enables • low-latency • low power(1000x) • low bandwidth model inference at edge devices. • Privacy • Can collect more data which helps to build better products.
4. Applications • Personal assistants like Google assistant, Alexa and Siri. • Intelligent industrial sensors • Wildlife tracking • Detecting crop diseases • Predicting wildfires • Smart game controllers • Ocean Life Conservation • Etc...
5. TensorFlow Lite • You can use this tool to wrap your models to run on embedded systems • It supports Android, IOS, Arduino etc.. • Not only Python you can use C, C++ and JAVA • Pretrained models • Other tools like • CoreML • PyTorch Mobile
6. TensorFlow Lite for Microcontrollers • Especially designed to run machine learning models on microcontrollers and other devices with only few kilobytes of memory. • TensorFlow Lite for Microcontrollers is written in C++ 11 and requires a 32-bit platform. • The framework is available as an Arduino library
7. Supported devices • Arduino Nano 33 BLE Sense • SparkFun Edge • STM32F746 Discovery kit • Adafruit EdgeBadge • Adafruit TensorFlow Lite for Microcontrollers Kit • Adafruit Circuit Playground Bluefruit • Espressif ESP32-DevKitC • Espressif ESP-EYE • Wio Terminal: ATSAMD51 • Himax WE-I Plus EVB Endpoint AI Development Board • Synopsys DesignWare ARC EM Software Development Platform
8. Workflow • Train your Machine learning models • Convert them to a TensorFlow Lite model using the TensorFlow Lite converter. • Convert to a C byte array using standard tools to store it in a read-only program memory on device. • Run inference on device using the C++ library and process the results.
9. Limitations of TF Lite for Microcontrollers • Support for a limited subset of TensorFlow operations • Support for a limited set of devices • Low-level C++ API requiring manual memory management • On device training is not supported
10. Complete project flow • Define the problem • Set up your hardware • Set up your software • Build and prepare the binary • Get ready to flash the binary • Flash the binary • Generate the output
11. Resources • HarvardX's Tiny Machine Learning (TinyML)
12. The Future of Machine Learning is Tiny and Bright
13. Thank you - Ask your questions