Edge AI for IoT Devices

Introduction

This project focuses on optimizing and deploying machine learning models on edge devices, enabling intelligent decision-making directly on IoT hardware without cloud connectivity. By combining model compression techniques with hardware acceleration, we achieve real-time AI inference on devices with limited computational resources.

Project Goals

• Ultra-low Power: ML inference under 1 mW power consumption
• Real-time Performance: Sub-10ms inference latency
• Small Footprint: Models under 100 KB
• Hardware Agnostic: Support for various microcontrollers and FPGAs

Technical Approach

1. Model Optimization Techniques

2. Hardware Platforms

Microcontrollers

• STM32 Series: Cortex-M4/M7 with DSP instructions
• ESP32: Dual-core with WiFi/Bluetooth
• nRF52840: BLE-enabled with Cortex-M4F

FPGAs

• Lattice iCE40: Ultra-low power FPGA
• Xilinx Spartan-7: Cost-effective acceleration
• Intel Cyclone: Edge AI optimized

3. Deployment Pipeline

# Model optimization and deployment workflow
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│ Training Model  │ --> │   Optimization  │ --> │   Compilation   │
│   (PyTorch)     │     │  (Quantization) │     │  (TensorFlow    │
└─────────────────┘     └─────────────────┘     │      Lite)      │
                                                 └─────────────────┘
                                                          │
                                                          v
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Verification   │ <-- │  Hardware       │ <-- │  C++ Runtime    │
│  & Testing      │     │  Deployment     │     │  Generation     │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Applications

1. Predictive Maintenance

Real-time vibration analysis for industrial equipment:

• Anomaly detection using autoencoders
• Remaining useful life prediction
• Edge-based fault classification

2. Smart Agriculture

Features:

• Disease detection from leaf images
• Soil moisture prediction
• Pest identification

3. Healthcare Monitoring

Wearable devices with on-device ML:

• ECG anomaly detection
• Fall detection using accelerometers
• Sleep pattern analysis

Performance Benchmarks

Code Examples

Model Quantization

import tensorflow as tf

def quantize_model(model_path):
    """Quantize a TensorFlow model for edge deployment"""
    converter = tf.lite.TFLiteConverter.from_saved_model(model_path)

    # Enable optimizations
    converter.optimizations = [tf.lite.Optimize.DEFAULT]

    # Set representative dataset for calibration
    converter.representative_dataset = representative_dataset_gen

    # Ensure integer only operations
    converter.target_spec.supported_ops = [
        tf.lite.OpsSet.TFLITE_BUILTINS_INT8
    ]
    converter.inference_input_type = tf.int8
    converter.inference_output_type = tf.int8

    # Convert model
    tflite_model = converter.convert()

    return tflite_model

Embedded Inference

// C++ inference engine for microcontrollers
class EdgeInference {
private:
    const tflite::Model* model;
    tflite::MicroInterpreter* interpreter;
    TfLiteTensor* input;
    TfLiteTensor* output;

public:
    EdgeInference(const uint8_t* model_data) {
        model = tflite::GetModel(model_data);

        // Create interpreter
        static tflite::MicroMutableOpResolver<10> resolver;
        resolver.AddConv2D();
        resolver.AddMaxPool2D();
        resolver.AddFullyConnected();
        resolver.AddSoftmax();

        static tflite::MicroInterpreter static_interpreter(
            model, resolver, tensor_arena, kTensorArenaSize
        );
        interpreter = &static_interpreter;

        interpreter->AllocateTensors();
        input = interpreter->input(0);
        output = interpreter->output(0);
    }

    float* predict(float* input_data) {
        // Copy input data
        memcpy(input->data.f, input_data, input->bytes);

        // Run inference
        interpreter->Invoke();

        return output->data.f;
    }
};

Development Tools

• TensorFlow Lite Micro: For microcontroller deployment
• Apache TVM: Hardware-agnostic compilation
• ONNX Runtime: Cross-platform inference
• Edge Impulse: End-to-end edge ML platform
• STM32Cube.AI: STM32-specific optimization

Future Work

1. Neuromorphic Computing: Exploring spiking neural networks
2. Federated Learning: Privacy-preserving model updates
3. Hardware-Software Co-design: Custom accelerators
4. Energy Harvesting: Self-powered ML devices

Resources

• GitHub Repository
• Documentation
• Tutorial Series

Collaboration

This project is open for collaboration. If you’re interested in:

• Contributing code or documentation
• Testing on new hardware platforms
• Sharing use cases or applications

Please reach out via email or open an issue on GitHub!