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: 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.