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 …

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 …

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 a technique for lightening the load of executing machine learning and artificial intelligence (AI) models. It aims to reduce the memory required for AI inference. Quantization …

Nov 24, 2025 · Quantization has emerged as a crucial technique to address this challenge, enabling resource-intensive models to run on constrained hardware. The NVIDIA TensorRT …

Oct 16, 2025 · Quantization is a data compression technique that allows machine learning models to operate more efficiently without substantial performance loss.

What is a Quantization? Quantization is the process of converting continuous data to discrete values. It's used in various fields like signal processing and machine learning to reduce data size.

Oct 25, 2025 · Quantization in signal processing refers to the process of mapping a continuous range of values (analog signals) into a finite set of discrete levels (digital signals).

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, …