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 with a finite …

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 efficient.

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-world …

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 memory …

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-world services.

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.

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.

Oct 16, 2025 · Quantization converts high-precision data into lower-precision data by compressing it to reduce data loss. By optimizing quantization, you can reduce your model's computational burden …

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 and breaking …