Nvidia corporation (20240112050). IDENTIFYING IDLE-CORES IN DATA CENTERS USING MACHINE-LEARNING (ML) simplified abstract
Contents
- 1 IDENTIFYING IDLE-CORES IN DATA CENTERS USING MACHINE-LEARNING (ML)
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IDENTIFYING IDLE-CORES IN DATA CENTERS USING MACHINE-LEARNING (ML) - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
IDENTIFYING IDLE-CORES IN DATA CENTERS USING MACHINE-LEARNING (ML)
Organization Name
Inventor(s)
Manas Ranjan Jagadev of San Jose CA (US)
IDENTIFYING IDLE-CORES IN DATA CENTERS USING MACHINE-LEARNING (ML) - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112050 titled 'IDENTIFYING IDLE-CORES IN DATA CENTERS USING MACHINE-LEARNING (ML)
Simplified Explanation
The patent application describes apparatuses, systems, and techniques to determine the number of idle cores of a computing device using a machine learning model based on a set of processes executed by the computing device.
- The method involves determining a set of processes executed by the computing device and using an ML model to determine the number of cores of the computing device to be powered down based on the set of processes.
- The method updates the number of cores from a first mode to a second mode where the number of cores consumes less power than in the first mode.
Potential Applications
This technology can be applied in:
- Energy-efficient computing
- Resource optimization in data centers
- Improving battery life in mobile devices
Problems Solved
This technology helps in:
- Reducing power consumption
- Optimizing performance based on workload
- Extending battery life of devices
Benefits
The benefits of this technology include:
- Cost savings on energy consumption
- Improved efficiency in computing devices
- Enhanced user experience with longer battery life
Potential Commercial Applications
- Cloud computing providers
- Mobile device manufacturers
- Data center operators
Possible Prior Art
One possible prior art could be techniques for dynamic voltage and frequency scaling in computing devices to optimize power consumption.
Unanswered Questions
How does this technology impact the overall performance of the computing device?
This article does not delve into the specific performance implications of powering down idle cores on the computing device. It would be interesting to know if there are any trade-offs in performance when implementing this power-saving technique.
What is the scalability of this technology for different types of computing devices?
The article does not address how this technology can be scaled for various computing devices, such as smartphones, laptops, or servers. Understanding the scalability of this innovation across different platforms would be crucial for its widespread adoption.
Original Abstract Submitted
apparatuses, systems, and techniques to determine a number of idle cores of a computing device using a machine learning (ml) model based on a set of processes executed by the computing device are described. one method determines a set of processes executed by the computing device and determines, using an ml model, a number of cores of the computing device to be powered down based at least on the set of processes. the method updates a first mode of the number of cores to a second mode in which the number of cores consumes less power than in the first mode.
