METHOD AND SYSTEM FOR DETERMINING A COMPRESSION RATE FOR AN AI MODEL OF AN INDUSTRIAL TASK

摘要

The invention regards a recommendation system and a method for determining a compression rate r* for an AI model of an industrial task (Task 1, ..., k; Task k+1, ..., k+n) according to a set of requirements for the runtime properties of the AI model, wherein, for the AI model, the original number of parameters (n) is reduced to a reduced number of parameters (n*). In a first stage each AI model is compressed with a number of different compression rates ri, wherein each of the compressed AI models is executed in an execution environment and as first results during the executions the runtime properties are recorded, and wherein an optimal compression rate r* for each of the AI models is calculated by an analytical analysis of the first results and stored in a database or similar system. In a second stage the data from the database is used to train an additional machine learning model. In a third stage for a new AI model of a new task (Task k+1, ..., k+n) a new set of desired runtime properties is defined and the additional machine learning model is employed for determining the optimal compression rate r* for that new AI model in respect to the desired runtime properties.