Description: Quantization Aware Training is an innovative technique that integrates quantization directly into the training process of machine learning models. This methodology allows models to learn to adapt to the precision limitations imposed by quantization, resulting in optimized performance when deployed on resource-constrained hardware. Quantization, in general terms, refers to the reduction of the precision of the numbers used in calculations, which can lead to a decrease in model size and an improvement in inference speed. However, this reduction in precision can negatively affect model accuracy. Quantization Aware Training addresses this challenge by allowing the model to adjust its parameters during training, taking into account the quantization constraints. This means that the model is trained not only to perform specific tasks but also to be robust against the loss of precision that may occur during quantization. This technique is especially relevant in the context of various deployment environments, including mobile devices and embedded systems, where efficiency and performance are crucial. In summary, Quantization Aware Training is a strategy that seeks to maximize the performance of machine learning models in environments where resources are limited, ensuring that model accuracy is maintained despite the reduction in numerical precision.
