Deploy with Edge Impulse¶

Technical Resources¶

Quick Reference¶

One-sentence definition: Edge Impulse is a development platform for building, training, and deploying machine learning models directly onto edge devices.
Key use cases: Simplifies building edge AI applications for IoT devices, sensors, and low-power hardware.
Prerequisites: Basic understanding of machine learning, edge devices, and Python.

Table of Contents¶

Introduction
Core Concepts
What is Edge Impulse?
Why Use Edge Impulse for Edge AI?
Workflow Overview
Visual Architecture
Edge Impulse Deployment Pipeline
Implementation Details
Example: Building and Deploying a Motion Classifier
Running Inference on an Edge Device
Tools & Resources
References

Introduction¶

What¶

Edge Impulse is a platform that enables developers to create efficient machine learning models optimized for real-time performance on edge devices.

Why¶

With Edge Impulse, developers can easily collect data, train models, and deploy them to constrained devices without deep expertise in ML.

Where¶

Applications include:
- Wearables: Activity recognition.
- IoT Sensors: Environmental monitoring.
- Industrial Automation: Fault detection.

Core Concepts¶

What is Edge Impulse?¶

Edge Impulse simplifies the edge AI workflow by providing tools for data collection, model training, optimization, and deployment.

Why Use Edge Impulse for Edge AI?¶

Ease of Use: No-code/low-code interface for beginners.
Optimization: Automatically generates lightweight models for edge hardware.
Cross-Platform: Supports deployment on various edge devices like Arduino, STM32, and Raspberry Pi.

Workflow Overview¶

Data Collection: Gather sensor or camera data.
Model Training: Use Edge Impulse’s interface to train and validate the model.
Deployment: Export optimized code for deployment.

Visual Architecture¶

Edge Impulse Deployment Pipeline¶

graph TD
A[Sensor Data] -->|Collect| B[Edge Impulse Studio]
B -->|Train| C[Trained Model]
C -->|Optimize| D[Deployed on Edge Device]

Implementation Details¶

Example: Building and Deploying a Motion Classifier¶

Step 1: Collect Data¶

Use a supported device (e.g., Arduino Nano 33 BLE Sense) to collect accelerometer data using Edge Impulse Studio.

Step 2: Train the Model¶

Upload the data to Edge Impulse Studio.
Select a motion classification model and train it.

Step 3: Export the Model¶

Choose the desired deployment format (C++ library, WebAssembly, or TensorFlow Lite).

Step 4: Deploy to Device¶

#include "model.h"  // Include Edge Impulse model header

void loop() {
    // Read sensor data
    float input_data[3] = {x, y, z};
    // Run inference
    EI_IMPULSE_RESULT result = run_classifier(input_data, sizeof(input_data));

    // Process the result
    Serial.print("Detected motion: ");
    Serial.println(result.classification[0].label);
}