18146776. RUNTIME-LEARNING GRAPHICS POWER OPTIMIZATION simplified abstract (Advanced Micro Devices, Inc.)
RUNTIME-LEARNING GRAPHICS POWER OPTIMIZATION
Organization Name
Inventor(s)
Rashad Oreifej of Orlando FL (US)
Sokratis Dimitriadis of Santa Clara CA (US)
Tzyy-Juin Kao of Santa Clara CA (US)
Xiayu Xu of Santa Clara CA (US)
RUNTIME-LEARNING GRAPHICS POWER OPTIMIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18146776 titled 'RUNTIME-LEARNING GRAPHICS POWER OPTIMIZATION
Simplified Explanation: The patent application describes systems, apparatuses, and methods for optimizing graphics power consumption during runtime by monitoring tasks queued for a computing component and adjusting operating frequencies based on task performance.
- Monitors tasks queued for a computing component
- Computes total clock cycles consumed for executing tasks
- Adjusts operating frequency while executing tasks to optimize power consumption
- Determines performance sensitivity of tasks based on clock cycle comparisons
Key Features and Innovation: - Monitoring and optimizing graphics power consumption in real-time - Adjusting operating frequencies based on task performance - Computing performance sensitivity of tasks for efficient power optimization
Potential Applications: - Mobile devices - Gaming consoles - Graphic design workstations
Problems Solved: - Excessive power consumption during graphics processing tasks - Inefficient use of computing resources - Lack of real-time power optimization solutions
Benefits: - Improved energy efficiency - Enhanced performance of computing components - Extended battery life for mobile devices
Commercial Applications: Optimizing graphics power consumption in mobile devices can lead to longer battery life and improved performance, making them more attractive to consumers. This technology can also be beneficial for gaming consoles and graphic design workstations, enhancing user experience and reducing energy costs.
Prior Art: Prior research in the field of power optimization for computing components may include studies on dynamic voltage and frequency scaling (DVFS) techniques, as well as research on task scheduling algorithms for power efficiency.
Frequently Updated Research: Researchers are continuously exploring new algorithms and techniques for optimizing power consumption in computing systems, including advancements in machine learning algorithms for dynamic power management.
Questions about Graphics Power Optimization: 1. How does this technology compare to existing power optimization techniques in terms of effectiveness? 2. What are the potential challenges in implementing real-time power optimization for graphics processing units?
Original Abstract Submitted
Systems, apparatuses, and methods for implementing runtime-learning graphics power optimization are illustrated. A system management unit monitors tasks queued for a computing component, such as a central processing unit (CPU) or a graphics processing unit (GPU). The system management unit computes a total number of clock cycles consumed to execute a first task. The system management unit then determines a second task for execution and modifies a current operating frequency by a given percentage while executing the second task. The system management unit determines the number of clock cycles that execution of the second task consumed and compares this to the number of clock cycles for the first task. Based at least in part on the comparison, the system management unit computes a performance sensitivity of tasks similar to the first and second tasks.
