Abstract: Disclosed Methods, Apparatus, and articles of manufacture to dynamically enable and/or disable prefetchers are disclosed. An example apparatus include an interface to access telemetry data, the telemetry data corresponding to a counter of a core in a central processing unit, the counter corresponding to a first phase of a workload executed at the central processing unit; a prefetcher state selector to select a prefetcher state for a subsequent phase based on the telemetry data; and the interface to instruct the core in the central processing unit to operate in the subsequent phase according to the prefetcher state.

Abstract: Described are mechanisms and methods for tracking user behavior profile over large time intervals and extracting observations for a user usage profile. The mechanisms and methods use machine learning (ML) algorithms embedded into a dynamic platform and thermal framework (DPTF) (e.g., Dynamic Tuning Technology) and predict device workloads using hardware (HW) counters. These mechanisms and methods may accordingly increase performance and user responsiveness by dynamically changing an Energy Performance Preference (EPP) based on a longer time workload analysis and workload prediction.

Abstract: In one embodiment, a method receives data regarding processing of a workload by a processor. The data is input into a prediction engine configured to classify the data into a plurality of workload classifications. Each workload classification describes different temporal behavior of the workload. Then, the method outputs a prediction for at least one of the plurality of workload classifications, wherein the prediction is used to control performance of the processor in an upcoming period of time.

Abstract: Disclosed Methods, Apparatus, and articles of manufacture to dynamically enable and/or disable prefetchers are disclosed. An example apparatus include an interface to access telemetry data, the telemetry data corresponding to a counter of a core in a central processing unit, the counter corresponding to a first phase of a workload executed at the central processing unit; a prefetcher state selector to select a prefetcher state for a subsequent phase based on the telemetry data; and the interface to instruct the core in the central processing unit to operate in the subsequent phase according to the prefetcher state.

Abstract: In one embodiment, a method receives data regarding processing of a workload by a processor. The data is input into a prediction engine configured to classify the data into a plurality of workload classifications. Each workload classification describes different temporal behavior of the workload. Then, the method outputs a prediction for at least one of the plurality of workload classifications, wherein the prediction is used to control performance of the processor in an upcoming period of time.