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AI for Edge Devices and Internet of Things

Edge AI enables real-time decision-making without dependence on cloud infrastructures, utilizing techniques like TinyML and model compression to operate on micro-devices. The interplay between model performance and efficiency is emphasized, as well as the importance of security and updates in production systems. Numerous industries benefit from edge computing, illustrating its versatile applications across various fields.

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Sections

  • 1

    What Is Edge Ai?

    Edge AI involves running AI algorithms locally on devices to enable real-time decision-making.

    Learning Practice

  • 2

    Edge Vs. Cloud Vs. Fog Computing

    This section explores the distinctions between edge, cloud, and fog computing with a focus on their individual characteristics and use cases.

    Learning Practice

  • 2.1

    Type

    Learning

  • 2.2

    Location

    This section covers the differences between edge, cloud, and fog computing, emphasizing the role and applications of edge AI.

    Learning Practice

  • 2.3

    Use Case

    This section highlights the significance of edge AI in various real-world applications, showcasing how it enhances decision-making and operational efficiency.

    Learning Practice

  • 3

    Model Optimization For Edge Ai

    This section covers techniques for optimizing AI models for deployment on edge devices, including quantization, pruning, knowledge distillation, and TinyML.

    Learning Practice

  • 3.1

    Quantization

    Quantization is a model optimization technique that reduces the precision of the model's parameters to enhance performance in edge computing.

    Learning Practice

  • 3.2

    Pruning

    Pruning is a model optimization technique used to enhance AI performance on edge devices by removing unnecessary weights and nodes.

    Learning Practice

  • 3.3

    Knowledge Distillation

    Knowledge distillation is a technique in machine learning that enables a smaller model to learn from a larger, well-trained model.

    Learning Practice

  • 3.4

    Tinyml

    TinyML is a subset of machine learning focused on deploying AI algorithms on ultra-low power devices, allowing for real-time insights in a variety of applications.

    Learning Practice

  • 3.5

    Libraries

    This section discusses the various libraries related to AI model optimization for edge devices and IoT applications.

    Learning Practice

  • 4

    Hardware Platforms For Edge Ai

    This section outlines key hardware platforms dedicated to deploying edge AI applications across various devices.

    Learning Practice

  • 4.1

    Nvidia Jetson

    NVIDIA Jetson is a powerful hardware platform designed for AI applications on edge devices, facilitating real-time processing in various industries.

    Learning Practice

  • 4.2

    Google Coral

    Google Coral is a hardware platform designed for deploying AI applications at the edge, enabling efficient processing of tasks directly on devices such as cameras and microcontrollers.

    Learning Practice

  • 4.3

    Raspberry Pi + Npu

    Raspberry Pi combined with Neural Processing Units (NPU) enhances AI capabilities for DIY Internet of Things (IoT) applications.

    Learning Practice

  • 4.4

    Arduino Nano 33 Ble

    The Arduino Nano 33 BLE is a powerful microcontroller designed for AI and IoT applications in edge computing.

    Learning Practice

  • 5

    Applications Of Edge Ai And Iot

    This section discusses various applications of Edge AI and IoT across different industries, highlighting specific use cases.

    Learning Practice

  • 5.1

    Smart Cities

    This section explores the integration of AI and IoT in smart cities, highlighting their applications and significance.

    Learning Practice

  • 5.2

    Healthcare

    This section discusses the application of Edge AI and IoT technologies in healthcare, focusing on how wearables and other technologies improve patient monitoring and diagnosis.

    Learning Practice

  • 5.3

    Agriculture

    This section discusses the applications of edge AI and IoT in agriculture, highlighting crop monitoring techniques.

    Learning Practice

  • 5.4

    Industry 4.0

    This section focuses on the integration of AI in edge computing and IoT within Industry 4.0, emphasizing real-time decision-making and optimized model deployment.

    Learning Practice

  • 5.5

    Retail

    This section discusses the applications of Edge AI in retail, particularly focusing on smart shelves and inventory tracking.

    Learning Practice

  • 6

    Challenges In Edge Ai

    This section discusses the key challenges faced in deploying AI on edge devices, focusing on hardware limitations, model accuracy, security vulnerabilities, and software compatibility.

    Learning Practice

  • 7

    Chapter Summary

    This chapter summary outlines the key concepts of Edge AI and its significance in real-time intelligent decision-making across various industries.

    Learning Practice

References

Chapter 9_ AI for Edge Devices and Internet of Things (IoT).pdf

Class Notes

Memorization

What we have learnt

  • Edge AI allows real-time de...
  • TinyML and model compressio...
  • Edge computing powers IoT s...

Final Test

Revision Tests

Chapter FAQs