Dynamic energy performance preference based on workloads using adaptive algorithm

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. 描述了用于在大的时间间隔上跟踪用户行为概况并且为用户使用概况提取观察结果的机制和方法。这些机制和方法使用嵌入到动态平台和热框架(DPTF)(例如，动态调谐技术)中的机器学习(ML)算法，并且使用硬件(HW)计数器来预测设备工作负载。这些机制和方法可相应地提高性能和用户响应能力，其方式是通过基于较长时间的工作负载分析和工作负载预测来动态地改变能量性能偏好(EPP)。