Abstract: Techniques are described for a hardware processor to dynamically configure a component that improves a processor function with a configuration setting based on invariant statistics. The invariant statistics are generated by execution of the instructions from one or more applications and are independent of the performance metrics of the processor function for the execution. In an embodiment, the configuration setting for the component is generated using a machine learning model.

Abstract: Techniques for generating a model for predicting when different hybrid prefetcher configurations should be used are disclosed. Techniques for using the model to predict when different hybrid prefetcher configurations should be used are also disclosed. The techniques for generating the model include obtaining a set of input data, and generating trees based on the training data. Each tree is associated with a different hybrid prefetcher configuration and the trees output certainty scores for the associated hybrid prefetcher configuration based on hardware feature measurements. To decide on a hybrid prefetcher configuration to use, a prefetcher traverses multiple trees to obtain certainty scores for different hybrid prefetcher configurations and identifies a hybrid prefetcher configuration to used based on a comparison of the certainty scores.

Abstract: Techniques for identifying a hardware configuration for operation are disclosed. The techniques include applying feature measurements to a trained model; obtaining output values from the trained model, the output values corresponding to different hardware configurations; and operating according to the output values, wherein the output values include one of a certainty score, a ranking, or a regression value.

Abstract: Techniques for generating a model for predicting when different hybrid prefetcher configurations should be used are disclosed. Techniques for using the model to predict when different hybrid prefetcher configurations should be used are also disclosed. The techniques for generating the model include obtaining a set of input data, and generating trees based on the training data. Each tree is associated with a different hybrid prefetcher configuration and the trees output certainty scores for the associated hybrid prefetcher configuration based on hardware feature measurements. To decide on a hybrid prefetcher configuration to use, a prefetcher traverses multiple trees to obtain certainty scores for different hybrid prefetcher configurations and identifies a hybrid prefetcher configuration to used based on a comparison of the certainty scores.