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Embedded Machine Learning

·1004 words·5 mins

Machine learning has traditionally been associated with powerful servers and cloud-based systems, but recent advancements have enabled the integration of machine learning models directly into embedded systems. This paradigm shift, known as Embedded Machine Learning (Embedded ML), is transforming how intelligent systems operate at the edge.

What is Embedded Machine Learning?
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Embedded Machine Learning involves running machine learning models on embedded systems, which are specialized computing systems designed to perform dedicated functions. These systems often have limited computational resources compared to traditional computers. By deploying ML models on these devices, we can achieve real-time data processing and decision-making without relying on cloud connectivity.

Applications in Mechatronics
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The field of mechatronics, which synergizes mechanical, electronic, computer, and control engineering, greatly benefits from Embedded ML. Here are a few applications:

1. Predictive Maintenance
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By embedding ML models into industrial machinery, we can predict equipment failures before they occur. Sensors on the machinery collect data, which the ML model analyzes in real-time to identify patterns indicative of impending failures. This approach minimizes downtime and reduces maintenance costs.

2. Autonomous Systems
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Embedded ML enables the development of autonomous robots and vehicles. For instance, drones equipped with ML models can analyze visual data to navigate and make decisions independently, making them ideal for applications in agriculture, surveillance, and delivery services.

3. Human-Machine Interaction
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Wearable devices and smart assistants use Embedded ML to enhance human-machine interactions. These devices can recognize voice commands, monitor health metrics, and provide personalized feedback, improving user experience and functionality.

How to Implement Embedded Machine Learning
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Implementing Embedded ML involves several steps:

1. Model Training
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Training ML models typically requires significant computational resources and large datasets. This step is usually performed on powerful computers or cloud-based platforms. Popular frameworks like TensorFlow and PyTorch offer tools to train models for embedded deployment.

2. Model Optimization
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Given the limited resources of embedded systems, it’s crucial to optimize ML models for efficiency. Techniques such as quantization, pruning, and model compression help reduce the model size and computational requirements without significantly compromising accuracy.

3. Deployment
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Deploying the optimized model onto the embedded device can be done using specialized software libraries and toolkits. For instance, TensorFlow Lite and PyTorch Mobile are designed for running ML models on embedded systems. These libraries provide APIs for integrating ML models into applications running on microcontrollers, single-board computers, and other edge devices.

4. Inference
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Once deployed, the embedded system performs inference, which is the process of making predictions using the ML model. This involves feeding input data into the model and interpreting the output to make decisions or trigger actions.

Example: Implementing Embedded ML on a Microcontroller
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Let’s consider a practical example of deploying an ML model on a microcontroller to detect anomalies in vibration data from a motor.

Step-by-Step Guide
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  1. Data Collection: Collect vibration data from the motor using an accelerometer sensor.

  2. Model Training: Use the collected data to train an anomaly detection model on a computer. Tools like TensorFlow or PyTorch can be used for this purpose.

  3. Model Optimization: Optimize the trained model using TensorFlow Lite to reduce its size and improve efficiency.

  4. Deployment: Load the optimized model onto a microcontroller like the Arduino Nano 33 BLE Sense, which has built-in support for TensorFlow Lite.

  5. Inference: Program the microcontroller to perform real-time inference on the incoming vibration data. If the model detects an anomaly, the system can trigger an alert.

Code Example
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Here’s a simplified code snippet for running a TensorFlow Lite model on an Arduino Nano 33 BLE Sense:

#include <TensorFlowLite.h>
#include "model.h"  // The optimized model binary

TfLiteTensor* input;
TfLiteTensor* output;

void setup() {
  // Initialize serial communication
  Serial.begin(115200);

  // Initialize the TensorFlow Lite interpreter
  static tflite::MicroInterpreter static_interpreter(
      model, tflite::ops::micro::AllOpsResolver(), tensor_arena, sizeof(tensor_arena), error_reporter);
  interpreter = &static_interpreter;

  // Allocate memory for the model's input and output tensors
  interpreter->AllocateTensors();

  // Get pointers to the input and output tensors
  input = interpreter->input(0);
  output = interpreter->output(0);
}

void loop() {
  // Read sensor data
  float sensor_data = readSensor();

  // Preprocess the data and fill the input tensor
  input->data.f[0] = sensor_data;

  // Run inference
  interpreter->Invoke();

  // Get the output and check for anomalies
  float anomaly_score = output->data.f[0];
  if (anomaly_score > THRESHOLD) {
    Serial.println("Anomaly detected!");
  }
}

float readSensor() {
  // Placeholder function to read data from the sensor
  return analogRead(A0) / 1023.0;
}

Case Study: Development of a UAV-Based Maize Leaf Disease Detection and Classification System
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In 2024, a group of 4 final year Mechatronics Engineering students in our University (Federal University of Agriculture, Abeokuta) researched on the development of a UAV-based maize leaf disease detection and classification system where they built a deep learning model with the YOLO v5 model that identifies three kinds of diseases (Common Rust, Northern Leaf Blight, and Gray Spot) on maize leaves, and reports the discovery to the user/farmer. You can read more about it here. Pictures will be available soon.

In this research, they trained their model on a computer but deployed it on a Raspberry Pi 5 which they can onboard as part of the UAV’s payload. Of course, they included the weight of the board in their UAV design calculations, allowing enough flight time and less work done by the UAV.

Resources and Opportunities in Embedded Machine Learning
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As the world advances today towards the integration of Artificial Intelligence, there is a growing need for mechatronics engineering experts to master the art of embedding machine learning models on microcontrollers. To get started, you can visit Edge Impulse for comprehensive tutorials on Tiny ML. They have free and paid products where they allow students and professionals to train models and deploy them on a wide range of supported boards.

Conclusion
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Embedded Machine Learning is revolutionizing the way we interact with and utilize edge devices. By bringing intelligence to the edge, we can create more responsive, efficient, and autonomous systems in the field of mechatronics. Whether it’s for predictive maintenance, autonomous navigation, or enhanced human-machine interaction, the possibilities are vast and continually expanding.