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 …

3 days ago · Quantization: the same model with lower precision A quantized model is the exact same model — with the same architecture and the same number of parameters — stored and …

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 …

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 …

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 a process of converting a continuous set of values (like all the possible real - number values) into a discrete set. In simple terms, it's like taking a wide range of possibilities …

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.