newdigital

With the advancement of machine learning and artificial intelligence technologies, there is a growing need to optimize processes for working with models. The efficiency of model operation directly depends on the data formats used to represent them. In recent years, several new data types have emerged, specifically designed for working with deep learning models.

In this article, we will focus on two such new data formats - float16 and float8, which are beginning to be actively used in modern ONNX models. These formats represent alternative options to more precise but resource-intensive floating-point data formats. They provide an optimal balance between performance and accuracy, making them particularly attractive for various machine learning tasks. We will explore the key characteristics and advantages of float16 and float8 formats, as well as introduce functions for converting them to standard float and double formats.

This will help developers and researchers better understand how to effectively use these formats in their projects and models. As an example, we will examine the operation of the ESRGAN ONNX model, which is used for image quality enhancement.

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