In mathematics and digital signal processing, quantization is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often …

Nov 6, 2025 · Quantization is a model optimization technique that reduces the precision of numerical values such as weights and activations in models to make them faster and more …

Quantization is the process of mapping continuous infinite values to a smaller set of discrete finite values. In the context of simulation and embedded computing, it is about approximating real …

2 days ago · Quantization reduces the precision of model parameters and activations (for example, from FP32/FP16 to FP8) to shrink memory footprint, improve inference speed, and …

What is quantization in machine learning? Quantization is a technique for lightening the load of executing machine learning and artificial intelligence (AI) models. It aims to reduce the …

Jul 20, 2025 · Among many optimization techniques to improve AI inference performance, quantization has become an essential method when deploying modern AI models into real …

This can be achieved via quantization. Quantization is a nonlinear and irreversible operation that maps a given amplitude x (n) at time t=nT into a value xn, that belongs to a finite set of values.

Uniform scalar quantization is the simplest and often most practical approach to quantization. Before reaching this conclusion, two approaches to optimal scalar quantizers were taken.

Quantization is representing the sampled values of the amplitude by a finite set of levels, which means converting a continuous-amplitude sample into a discrete-time signal.

Quantization is the process of reducing the precision of a digital signal, typically from a higher-precision format to a lower-precision format. This technique is widely used in various fields, …