TensorFlow Model Optimization Toolkit-Quantization

TensorFlow Model Optimization Toolkit (TF MOT) is a set of tools designed to optimize and compress TensorFlow models. One of the most powerful features of TF MOT is quantization, which is the process of converting floating-point weights and activations in a neural network to lower-precision fixed-point numbers. This reduces the memory and computation requirements of the network, making it more efficient to run on resource-constrained devices like mobile phones and embedded systems.

In this story, I will explore the basics of quantization using TF MOT, including its benefits, different types of quantization, and how to implement it in your TensorFlow models.

Why use quantization?

The primary advantage of quantization is that it reduces the memory and computation requirements of a neural network. This is especially important for mobile and embedded devices, which have limited resources compared to desktop and server environments. By converting floating-point numbers to fixed-point numbers, the size of the model can be significantly reduced, allowing it to fit on a smaller device and run more efficiently.

Another benefit of quantization is that it can improve the accuracy of a neural network. This may seem counterintuitive, as fixed-point numbers are less precise than floating-point numbers. However, the process of quantization can actually help regularize the network and prevent overfitting, leading to better generalization performance on new data.

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Types of quantization

There are several types of quantization that can be applied to a neural network, depending on the precision of the fixed-point numbers used. Here are the most common types:

1. Integer quantization: In this method, the weights and activations are quantized to 8-bit integers. This method can be used for both post-training quantization and quantization-aware training. During post-training quantization, the weights and activations are quantized after the model has been trained. During quantization-aware training, the model is trained with the knowledge that it will be quantized later, allowing the model to learn how to operate with lower precision.
2. Dynamic range quantization…