Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

Model quantization represents model weights, activations, or cache values with fewer bits to reduce memory traffic, storage, energy, and often inference latency. This guide explains the mechanism, ...

Model quantization bridges the gap between the computational limitations of edge devices and the demands for highly accurate models and real-time intelligent applications. The convergence of ...

The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. In this blog, we will talk about quantization in ...