Quantization

Machine learning and artificial intelligence models are revolutionizing data management. AI models can process vast amounts of data with remarkable speed, but they demand significant resources and computational power. 

Quantization, a technique for minimizing the demand for computation resources by processing data more efficiently, addresses this challenge.

What is quantization?

Quantization definition: Within the field of ​​machine learning, quantization is the process of reducing the precision of a model’s parameters to speed up computation. 

Quantization converts high-precision data into lower-precision data, making it simpler and faster to process, thereby improving the overall computation speed of the model. 

Reducing the precision in the parameters also reduces accuracy. So, data engineers must balance maintaining accuracy with minimizing unnecessary precision that adds computational overhead. 

Quantization is often used with large language models (LLMs) to reduce the data processing demands of models that handle extensive, complex data like language. For example, AI chatbots require significant computational power for the inference process. Using quantization reduces the amount of computational power needed. 

How does quantization work? 

Within a ​neural network, high-precision data might be represented as a 32-bit floating point value (FP32), while lower-precision data could be an 8-bit integer (INT8). FP32 can represent billions of potential values while INT8 can represent fewer than 300 potential values. This option makes the data much simpler and therefore quicker to process. 

Quantization uses specific algorithms to lower the data precision accurately. Several models can be used, but two common ones are absolute max quantization and affine quantization. 

In absolute max quantization, calculations begin by dividing the maximum possible value of the tensor before multiplying by the data range. A tensor is a multidimensional array used to represent data in neural networks, allowing for efficient computation and manipulation of large datasets. 

In affine quantization, calculations follow a specific algorithm seeking to establish outliers, which can then be mapped within the regular range of the data. 

These models calculate the equivalent value of the data in a lower-precision format, enabling an accurate conversion between high-precision and low-precision data values. 

What is the history of quantization?

Quantization within the field of AI is distinct from its origins in physics. In physics, quantization refers to the transition from classical to quantum mechanics, a concept developed by pioneers like Max Planck, Albert Einstein and Niels Bohr in the early 20th century.

In contrast, AI quantization has emerged more recently with the rapid growth of artificial intelligence technologies. It has become a core aspect of machine learning, developed to expedite computation and achieve faster results. 

What are key types of quantization?

Quantization is a crucial technique in optimizing AI models for efficiency and performance. There are several key types of quantization, each with its own advantages and trade-offs: 

• Post-training quantization (PTQ) takes place after the model has been trained. It applies quantization to an existing data model, converting the data to a lower-precision value without retraining the model. PTQ is a faster process than other quantization methods, but it can also reduce accuracy as it shrinks the existing model. 
• Quantization-aware training (QAT) takes the value conversion of quantization into account while training the model on data. As opposed to post-training quantization, QAT trains the model on the final data in the quantized form. This method produces higher output performance since the model has been accurately trained. However, it requires more resources and time.

• Dynamic quantization calibrates data during the quantization process. The clipping range is computed dynamically for different activation values, allowing the quantization to be specifically tailored for each data point. 
• Static quantization uses a fixed clipping range for all inputs, regardless of their individual activation values. This method is less flexible but can be more efficient in terms of computational cost. 

​How is quantization used?

Many ​​large language models use quantization to make data processing and output more efficient. It enables LLMs to process similar datasets with a high level of accuracy while reducing processing time to ensure quick output. 

Mobile applications can use quantization technology to enhance AI features without overburdening the computational demand on mobile devices. With quantization, many mobile applications can run real-time artificial intelligence programs efficiently without draining resources or experiencing lagging responses. 

Similarly, quantization technology can be applied to autonomous vehicles to maximize effectiveness and efficiency without relying on computational power or a lengthy response time. With quantization, the artificial intelligence models within autonomous vehicles can gather and respond to new data in real time.