Hardware resource configuration for processing system

A computer-implemented training method for training a machine learning model for controlling hardware resource configuration for a processing system comprising at least one processor core;

• the method comprising:
• for each of a plurality of workloads or groups of workloads selected as a selected workload or selected group of workloads, performing a training operation comprising:
• obtaining performance monitoring data indicative of processing performance for the selected workload or selected group of workloads when processed in a plurality of different hardware resource configurations of at least one processor core, the plurality of hardware resource configurations including hardware resource configurations in which different amounts of hardware resource are made available for use when processing the selected workload or selected group of workloads;
• training the machine learning model to adapt model parameters for the machine learning model based on the performance monitoring data for the selected workload or selected group of workloads and the plurality of different hardware resource configurations;
• and
• recording trained model parameters for the machine learning model resulting from the training operation performed for the plurality of workloads or groups of workloads.

A processing system comprising:

• at least one processor core;
• performance monitoring circuitry to obtain performance monitoring data indicative of processing performance associated with workloads to be executed on the processing system;
• and
• configuration control circuitry to set, based on an inference made by a trained machine learning model from input data depending on the performance monitoring data, control information for controlling an amount of hardware resource allocated for use by the at least one processor core.