Tutorial: Full Integer Quantization and Hybrid Quantization

Related Article: A Comprehensive Guide to TensorFlow Quantization

Quantization is a crucial technique in machine learning and deep learning to reduce the memory and computational requirements of neural networks. In this tutorial, we will explore two common quantization techniques: Full Integer Quantization and Hybrid Quantization. We will explain each of these techniques, provide a comparison, offer a hands-on coding example, and conclude with a FAQ section.

1. Quantization

1.1 Full Integer Quantization

Full Integer Quantization involves converting the weights and activations of a neural network from floating-point numbers to fixed-point integers. This technique significantly reduces the memory and computational requirements, making it suitable for deploying models on resource-constrained devices.

1.2 Steps for Full Integer Quantization:

1. Training the Model: Train your neural network as usual with floating-point precision.

2. Quantization-Aware Training: To improve the quantization process, you can use quantization-aware training. This trains the model with the knowledge that quantization will occur during inference, helping the model…