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
   

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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
   

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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.
   

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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...
   

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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
   

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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
   

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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
   

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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.
   

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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
   

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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
    

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11. Resources
    • HarvardX's Tiny Machine Learning (TinyML)
    

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12. The Future of
    Machine Learning
    is Tiny and Bright
    

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13. Thank you
    - Ask your questions
    

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