Quantization

See also: Machine learning terms

Quantization in Machine Learning

Quantization is a technique utilized in machine learning and deep learning to reduce the size of models and computational resources needed for their operation. The process entails approximating the continuous values of parameters, such as weights and activations, using a smaller, discrete set of values. Quantization is particularly useful in deploying models on resource-constrained devices, like mobile phones and embedded systems, by improving efficiency and reducing energy consumption.

Types of Quantization

There are two primary types of quantization: weight quantization and activation quantization.

Weight Quantization

Weight quantization focuses on reducing the bit width of the weights in the model. This approach reduces the overall memory footprint and accelerates the computations by using lower-precision arithmetic. Commonly used weight quantization techniques include:

• Binary quantization: The weights are approximated using only two possible values, typically -1 and 1.
• Ternary quantization: This method represents weights with three possible values, often -1, 0, and 1.
• Fixed-point quantization: Weights are represented using a fixed number of bits, resulting in a limited range and granularity of values.

Activation Quantization

Activation quantization is the process of approximating the continuous activation values produced by the neurons in a network. By quantizing activations, it is possible to reduce the required storage and computational resources during the forward pass and backpropagation. Commonly used activation quantization techniques include:

• Binary quantization: Activations are represented using only two possible values, such as -1 and 1.
• Fixed-point quantization: Similar to weight quantization, activations are represented using a fixed number of bits, resulting in a limited range and granularity of values.

Quantization-Aware Training

Quantization-aware training is a training strategy that incorporates the effects of quantization during the training process itself. By simulating the quantization process during training, the model is more robust and can better adapt to the errors introduced by quantization. This strategy typically yields higher accuracy when compared to quantizing a pre-trained model.

Explain Like I'm 5 (ELI5)

Imagine you have a box full of colorful balls, and you can only choose a few of them to play with. In machine learning, quantization is like picking only a few specific colors of balls to represent all the colors. By doing this, you can save space and make it easier to play with the balls, just like how quantization helps make machine learning models smaller and faster to use.