Method and System for Determining a Compression Rate for an AI Model of an Industrial Task

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 runtime properties of the AI model, an original number of parameters (n) being reduced to a reduced number of parameters (n*) for the AI model, the method comprising:
• determining, in a first stage, a number of different compression rates ri for the assigned AI model for a number of different AI models for a number of industrial tasks (Task 1, ..., k) for each of the industrial tasks (Task 1, ..., k), each AI model with that number of different compression rates ri being compressed, the compressed AI models being executed in an execution environment, the runtime properties being recorded as first results during execution of each of the AI models, an analytical analysis of the first results being performed to calculate an optimal compression rate r* for each of the AI models, the optimal compression rate r* for each industrial task (Task 1, ..., k) together with a description of the industrial task being stored in a database;
• utilizing, in a second stage, the data from the database to train an additional machine learning model, the additional machine learning model having feature information about each of the industrial tasks (Task 1, ..., k) as an input and the calculated optimal compression rate r* as an output, the feature information at least comprising information of memory allocation limit, inference time limit for the compressed model, an original AI model size of the uncompressed AI model, and a compression algorithm utilized;
• and
• defining, 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 and employing an additional machine learning model for determining an optimal compression rate r* for that new AI model with respect to desired runtime properties.

A system 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 runtime properties of the AI model, an original number of parameters (n) being reduced to a reduced number of parameters (n*) for the AI model, the system comprising:
• a first computer system (CS1) configured to:
• determine, in a first stage, a number of different compression rates ri for the assigned AI model for a number of different AI models for a number of industrial tasks (Task 1, ..., k) for each of the industrial tasks (Task 1, ..., k), each AI model with that number of different compression rates ri being compressed, the compressed AI models being executed in an execution environment, the runtime properties being recorded as first results during execution of each of the AI models, an analytical analysis of the first results being performed to calculate an optimal compression rate r* for each of the AI models, the optimal compression rate r* for each industrial task (Task 1, ..., k) together with a description of the industrial task being stored in a database, the first computer system (CS1) being further configured to control the execution environment while execution of the compressed AI models occurs;
• a second computer system (CS2) configured to:
• utilize, in a second stage, the data from the database to train an additional machine learning model, the additional machine learning model having feature information about each of the industrial tasks (Task 1, ..., k) as an input and the calculated optimal compression rate r* as an output, the feature information at least comprising information of memory allocation limit, inference time limit for the compressed model, an original AI model size of the uncompressed AI model, and a compression algorithm utilized;
• and
• define, 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 and employing an additional machine learning model for determining an optimal compression rate r* for that new AI model with respect to desired runtime properties;
• and
• a communication channel connecting the first and the second computer systems (CS1, CS2).

A non-transitory computer-readable program product, encoded with computer readable program code which, when executed by a processor on a computer, determines 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 runtime properties of the AI model, an original number of parameters (n) being reduced to a reduced number of parameters (n*) for the AI model, the computer readable program code comprising:
• program code for determining, in a first stage, a number of different compression rates ri for the assigned AI model for a number of different AI models for a number of industrial tasks (Task 1, ..., k) for each of the industrial tasks (Task 1, ..., k), each AI model with that number of different compression rates ri being compressed, the compressed AI models being executed in an execution environment, the runtime properties being recorded as first results during execution of each of the AI models, an analytical analysis of the first results being performed to calculate an optimal compression rate r* for each of the AI models, the optimal compression rate r* for each industrial task (Task 1, ..., k) together with a description of the industrial task being stored in a database;
• program code for utilizing, in a second stage, the data from the database to train an additional machine learning model, the additional machine learning model having feature information about each of the industrial tasks (Task 1, ..., k) as an input and the calculated optimal compression rate r* as an output, the feature information at least comprising information of memory allocation limit, inference time limit for the compressed model, an original AI model size of the uncompressed AI model, and a compression algorithm utilized;
• and
• program code for defining, 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 and employing an additional machine learning model for determining an optimal compression rate r* for that new AI model with respect to desired runtime properties.