Google llc (20240185030). ADJUSTING NEURAL NETWORK RESOURCE USAGE simplified abstract
Contents
- 1 ADJUSTING NEURAL NETWORK RESOURCE USAGE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ADJUSTING NEURAL NETWORK RESOURCE USAGE - 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
ADJUSTING NEURAL NETWORK RESOURCE USAGE
Organization Name
Inventor(s)
Augustus Quadrozzi Odena of San Francisco CA (US)
John Dieterich Lawson of Palo Alto CA (US)
ADJUSTING NEURAL NETWORK RESOURCE USAGE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185030 titled 'ADJUSTING NEURAL NETWORK RESOURCE USAGE
Simplified Explanation
The patent application describes methods, systems, and apparatus for adjusting neural network resource usage. One method involves receiving a network input for processing by a task neural network, receiving a usage input specifying weights for usage factors that impact computational resource usage, and processing the network input using only selected neural network layers based on the usage input.
- Neural network resource usage adjustment method
- Receive network input for processing by task neural network
- Receive usage input specifying weights for usage factors
- Usage factors impact computational resource usage
- Process network input using selected neural network layers based on usage input
Potential Applications
The technology can be applied in various fields such as artificial intelligence, machine learning, data processing, and image recognition.
Problems Solved
The technology helps in optimizing computational resource usage in neural networks, leading to improved efficiency and performance.
Benefits
The benefits of this technology include enhanced resource management, increased processing speed, and improved accuracy in neural network tasks.
Potential Commercial Applications
Potential commercial applications of this technology include AI-powered systems, data analytics platforms, and image processing software.
Possible Prior Art
One possible prior art could be the use of dynamic neural network architectures to optimize resource usage in machine learning tasks.
== What are the potential limitations of this technology? Potential limitations of this technology could include the complexity of implementing dynamic resource allocation algorithms and the need for extensive testing to ensure optimal performance.
== How does this technology compare to existing methods for adjusting neural network resource usage? This technology offers a more efficient and flexible approach to adjusting neural network resource usage compared to traditional static resource allocation methods.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for adjusting neural network resource usage. one of the methods includes receiving a network input for processing by a task neural network, the task neural network comprising a plurality of neural network layers; receiving a usage input specifying a respective weight for each of one or more usage factors, wherein each usage factor impacts how many computational resources are used by the task neural network during the processing of the network input; and processing the network input using the task neural network in accordance with the usage input to generate a network output for the network input, comprising: selecting, based at least on the usage input, a proper subset of the plurality of neural network layers to be active while processing the network input, and processing the network input using only the selected neural network layers.
