Adding Intelligence to the Edge Devices with Cloud IoT - Devfest FL

Adding Intelligence to the Edge Devices with Cloud IoT - Devfest FL

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Sivamuthu Kumar

November 16, 2019
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Transcript

  1. Adding Intelligence to the Edge Devices with Cloud IoT DEVFEST

    FL NOV 16, 2019
  2. Agenda Cloud IoT Introduction Adding Intelligence to the Edge Devices

    Coral Dev board & USB Accelerator Demo
  3. HELLO! I’m SivamuthuKumar Architect - Computer Enterprises Inc Orlando, FL

    #Cloud #Mobile #IoT ksivamuthu
  4. IoT INTERNET OF THINGS

  5. IoT is the concept of connecting any device to the

    Internet and to other connected devices
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  8. It is predicted that there will be 41.6 billion connected

    IoT devices, or "things," generating 79.4 zettabytes (ZB) of data in 2025 This Photo by Unknown Author is licensed under CC BY-NC-ND
  9. Cloud IoT Core DATA INGESTION AND DEVICE MANAGEMENT

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  11. Cloud IoT Core Bi-directional communication with billions of IoT devices

    ◦ Device-to-cloud telemetry data, cloud-to-device command, track message delivery Work with familiar platform and protocols ◦ HTTP, MQTT protocols and clients Security Enhanced Solutions ◦ Individual identities and credentials for each of connected devices. Automate device provisioning to accelerate IoT deployment ◦ Register and provision devices with zero touches, in a highly secure and scalable way. Logging and Monitoring ◦ Audit logs and device logs
  12. Cloud ML Fully managed service Preprocess and Orchestrate ML Workflow

    as Dataflow pipeline Analyze data and develop ML models in Datalab Training at scale using TensorFlow Batch and online predictions using REST at scale
  13. ML Processing Units

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  15. Cloud IoT Edge

  16. Why ML at Edge? LATENCY BANDWIDTH PRIVACY CONNECTIVITY EXECUTION COST

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  18. Edge TPU GOOGLE’S PURPOSE-BUILT ASIC DESIGNED TO RUN INFERENCE AT

    THE EDGE.
  19. Edge TPU Development Kits Google Coral Dev Board USB Accelerator

    An individual Edge TPU is capable of performing 4 trillion operations (tera-operations) per second (TOPS), using 0.5 watts for each TOPS (2 TOPS per watt).
  20. Demo RASPBERRY PI4 AND CORAL USB ACCELERATOR

  21. Donuts This Photo by Unknown Author is licensed under CC

    BY-SA This Photo by Unknown Author is licensed under CC BY-SA
  22. Cloud IoT Edge –Three parts TFLite Models ( Compatible with

    Edge TPU ) Hardware Coral Dev board / USB Accelerator Edge TPU Runtime
  23. Cloud AutoML Vision

  24. Hardware ØAny Linux computer x86-64 or ARM64 system architecture ØOne

    available USB Port ØGoogle’s Coral USB Accelerator
  25. Edge TPU Runtime

  26. Edge TPU Delegates Install the TensorFlow Lite runtime Library Run

    the python Interpreter API with experimental Edge delegates. TF Lite API or Edge TPU API:
  27. Boat Classification

  28. Object Detection

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  31. Key Takeaways High speed ML inference using Edge TPU on

    low power devices. Cloud AutoML to train and export into TFLite and TFLite Edge optimized models. Retrain / Transfer Learning using AutoML or on your own and compile into Edge TPU TFLite models Edge TPU with Coral SoM or USB Accelerator ( USB3.0 & Host computing)
  32. Reference Demo: https://github.com/ksivamuthu/cloud-edge-tpu-demo Slides: https://speakerdeck.com/ksivamuthu/adding-intelligence- to-the-edge-devices-with-cloud-iot Cloud IoT Core: https://cloud.google.com/iot-core/

    Coral Dev Kit & Accelerator: https://coral.withgoogle.com/products/accelerator/
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  34. Thank you ksivamuthu