Edge AI Tinyml

Edge AI and TinyML

Deploying ML models to edge devices — from model optimization (quantization, pruning) through TensorFlow Lite Micro, ONNX Runtime, and deployment on MCUs and edge hardware.

When to Use

• Running ML models on resource-constrained devices
• Reducing cloud dependency and latency
• Privacy-preserving on-device inference
• IoT sensor data processing at the edge

Edge ML Pipeline

class EdgeMLOptimizer:
    """Optimize models for edge deployment."""
    
    @staticmethod
    def quantize_to_int8(model_path: str, output_path: str, 
                          representative_dataset) -> str:
        import tensorflow as tf
        converter = tf.lite.TFLiteConverter.from_saved_model(model_path)
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        converter.representative_dataset = representative_dataset
        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
        tflite_model = converter.convert()
        
        with open(output_path, 'wb') as f:
            f.write(tflite_model)
        return output_path
    
    @staticmethod
    def estimate_memory(model_path: str) -> Dict:
        import os
        size = os.path.getsize(model_path)
        return {'model_size_bytes': size, 'model_size_kb': round(size / 1024, 1)}

Verification Checklist

• Quantization method chosen (INT8, FP16) for target hardware
• Model fits within edge device memory (RAM + flash)
• Inference latency acceptable for use case
• Accuracy validated post-quantization
• Hardware support verified (TFLite Micro, ONNX Runtime, or vendor SDK)
• Power consumption measured (battery-powered devices)
• OTA update mechanism for model updates