Quantization in Machine Learning: Making Big Models Smaller and Faster- An Overview
Are you tired of waiting for your machine learning models to run? Do you wish you could make them smaller and faster without sacrificing accuracy? Then it’s time to explore the exciting world of quantization!
What is quantization?
In the context of machine learning, it means reducing the precision of the parameters and/or activations in a neural network. Instead of using 32-bit floating point numbers, which can be memory-intensive and slow to compute, we can use 8-bit integers or even binary values (0 or 1). This allows us to represent the same information with fewer bits, leading to smaller model sizes and faster inference times.
Why do we need quantization?
There are several reasons why quantization is becoming increasingly important in machine learning. First, as models become larger and more complex, they require more memory and computational resources to train and run. This can be a bottleneck, especially on resource-constrained devices such as mobile phones and embedded systems.
- Quantization can help reduce the memory footprint and speed up inference, making it possible to deploy models on a wider range of devices.
- Some hardware platforms, such as GPUs and TPUs, have dedicated hardware support for low-precision operations. By quantizing our models, we can take advantage of this hardware acceleration and achieve even faster performance.
- Quantization can also improve the energy efficiency of machine learning applications. By using fewer bits to represent each value, we reduce the amount of power needed to perform computations, which is especially important for battery-powered devices.
Types of quantization
Post-training quantization and quantization-aware training are two techniques used in machine learning to achieve the benefits of quantization.
Post-training quantization involves applying quantization to a trained model.
Quantization-aware training, on the other hand, involves training a model with quantization in mind. This means that the model is trained to perform well even when its weights and activations are represented using low-precision values.
I will explain the types of quantization in detail in a separate article. Stay tuned!!!
In conclusion, quantization is a powerful technique that can help make our machine learning models smaller, faster, and more energy-efficient.
