Neural Networks on the Edge: Quantization Techniques

The Edge Computing Challenge

Running neural networks on mobile and IoT devices requires aggressive optimization. This guide covers practical quantization techniques for 10x+ inference speedup.

INT8 Quantization

Converting FP32 weights to INT8 reduces model size by 75%:

import tensorflow as tf

# Load your trained model
model = tf.keras.models.load_model('model.h5')

# Convert to TFLite with INT8 quantization
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.int8]

# Provide representative dataset
def representative_dataset():
    for _ in range(100):
        yield [np.random.rand(1, 224, 224, 3).astype(np.float32)]

converter.representative_dataset = representative_dataset
tflite_model = converter.convert()

Results

Model	Size	Latency (ms)	Accuracy
FP32	89MB	245ms	94.2%
INT8	23MB	18ms	93.8%

Conclusion

Edge deployment is now practical for most mobile use cases with proper quantization.