Intel corporation (20240354559). TOOL FOR FACILITATING EFFICIENCY IN MACHINE LEARNING simplified abstract

TOOL FOR FACILITATING EFFICIENCY IN MACHINE LEARNING

Organization Name

intel corporation

Inventor(s)

Rajkishore Barik of Santa Clara CA (US)

Brian T. Lewis of Palo Alto CA (US)

Murali Sundaresan of Sunnyvale CA (US)

Jeffrey Jackson of Newberg OR (US)

Feng Chen of Shanghai (CN)

Xiaoming Chen of Shanghai (CN)

Mike Macpherson of Portland OR (US)

TOOL FOR FACILITATING EFFICIENCY IN MACHINE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240354559 titled 'TOOL FOR FACILITATING EFFICIENCY IN MACHINE LEARNING

Simplified Explanation

The mechanism described in the patent application helps optimize the distribution of resources for deep learning autonomous machines by introducing a library to a neural network application to determine the best point for frequency scaling without affecting performance.

• Detecting sets of data from various sources over networks
• Introducing a library to a neural network application
• Determining optimal point for frequency scaling without performance degradation

Key Features and Innovation

• Smart distribution of resources for deep learning autonomous machines
• Introducing a library to optimize frequency scaling in neural network applications
• Detection of data sets from multiple sources over networks

Potential Applications

This technology can be applied in various industries such as autonomous vehicles, robotics, healthcare, and finance for optimizing deep learning processes.

Problems Solved

This technology addresses the challenge of efficiently distributing resources for deep learning autonomous machines without compromising performance.

Benefits

• Improved efficiency in resource distribution
• Enhanced performance of neural network applications
• Optimal frequency scaling without degradation

Commercial Applications

• Autonomous vehicles for improved decision-making processes
• Robotics for enhanced automation capabilities
• Healthcare for advanced diagnostic tools
• Finance for optimized trading algorithms

Prior Art

Researchers can explore prior art related to resource optimization in deep learning and neural networks to understand the evolution of this technology.

Frequently Updated Research

Stay updated on the latest advancements in resource optimization for deep learning autonomous machines to enhance the application of this technology.

Questions about Resource Optimization for Deep Learning Autonomous Machines

How does resource optimization impact the performance of neural network applications?

Resource optimization plays a crucial role in improving the efficiency and performance of neural network applications by ensuring that resources are allocated effectively.

What are the key factors to consider when determining the optimal point for frequency scaling in neural network applications?

The key factors include the type of data being processed, the computing device's capabilities, and the specific requirements of the neural network application.

Original Abstract Submitted

a mechanism is described for facilitating smart distribution of resources for deep learning autonomous machines. a method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and introducing a library to a neural network application to determine optimal point at which to apply frequency scaling without degrading performance of the neural network application at a computing device.