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Review Assignment Due Date Open in Codespaces

Assignment: Build and Deploy a Keyword Spotting Model using Edge Impulse

Overview

This assignment introduces you to the exciting world of TinyML and edge AI hardware by guiding you through the process of building a keyword-spotting system using Edge Impulse. You will deploy your model on both a smartphone and an Arduino Nano 33 BLE Sense board to evaluate its performance and usability on resource-constrained hardware.

Objectives

  1. Learn how to build and train a keyword-spotting model using Edge Impulse.
  2. Deploy the trained model to both a smartphone and embedded hardware (Arduino Nano 33 BLE Sense).
  3. Analyze and report on the model's performance, including trade-offs between accuracy and inference speed.

Steps to Complete the Assignment

Part 1: Train and Deploy Your Model

  1. Create an Edge Impulse Account:

  2. Follow the Tutorial:

  3. Deploy to Your Smartphone:

  4. Bring Your Laptop:

    • Deployment is quick (less than an hour), but ensure you have your laptop with you.

Part 2: Deploy to Arduino Nano 33 BLE Sense

  1. Required Hardware:

    • Use the Arduino Nano 33 BLE Sense board, (only 2 boards available) as part of the TinyML Arduino Learning Kit, which includes:
      • 1 Arduino Nano 33 BLE Sense board
      • 1 OV7675 Camera
      • 1 Arduino Tiny Machine Learning Shield
      • 1 USB A to Micro USB Cable
        (Use only the Arduino Nano 33 BLE Sense board for this assignment.)

  2. Install the Arduino IDE:

  3. Set Up Arduino IDE:

    • In Arduino IDE, navigate to Tools > Boards > Boards Manager, search for nano 33, and install Arduino Mbed OS Nano Boards.
    • Select the Nano 33 BLE Sense board from Tools > Board.

  4. Prepare and Deploy the Model:

    • After training in Edge Impulse:
      • Navigate to the Deployment page.
      • Select Arduino Library and choose INT8 Quantization.
      • Click Build to download the deployment ZIP file.
    • In Arduino IDE:
      • Include the ZIP file as a library: Sketch > Include Library > Add .zip Library.
      • Exit and re-enter the Arduino IDE to ensure the library is loaded.
      • Open the example code from:
        File > Examples > YOUR_PROJECT_NAME_inferencing > nano_ble_33_sense > nano_ble_33_sense_microphone_continuous.
      • Verify and upload the code to the board.
      • Open the serial monitor (Tools > Serial Monitor) to observe outputs.
        (First-time compilation may take ~20 minutes.)

Part 3: Resources

  • How to Deploy a Model to Arduino:
    YouTube Video
  • TinyML on Arduino Using Edge Impulse:
    Cytron Blog Post (Optional: Try controlling an LED with voice commands.)

Deliverables

Submit a PDF file with the following:

  1. Questions:
    • Does the model perform as accurately as expected on your smartphone? List a few methods to improve the model's accuracy.
    • When building a model for resource-limited hardware, how do you balance fast inference times with acceptable model accuracy? What trade-offs did you encounter?
    • Include screenshots of the training performance from step 6 of the deployment process.
  2. Videos:
    • Record and provide links to:
      • The keyword-spotting model working on your smartphone.
      • The keyword-spotting model working on the embedded Arduino board.
  3. Reflections:
    • Share your experience deploying the model to your smartphone and Arduino board. Mention any technical difficulties or interesting observations.
  1. Compilation Time:
    • Be patient during the Arduino IDE's first compilation; it can take up to 20 minutes.

Happy experimenting!