Advanced micro devices, inc. (20240111840). Selecting a Tiling Scheme for Processing Instances of Input Data Through a Neural Netwok simplified abstract
Contents
- 1 Selecting a Tiling Scheme for Processing Instances of Input Data Through a Neural Netwok
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Selecting a Tiling Scheme for Processing Instances of Input Data Through a Neural Netwok - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
Selecting a Tiling Scheme for Processing Instances of Input Data Through a Neural Netwok
Organization Name
Inventor(s)
Akila Subramaniam of Allen TX (US)
Tung Chuen Kwong of Richmond Hill (CA)
Juanjo Noguera of Santa Clara CA (US)
Selecting a Tiling Scheme for Processing Instances of Input Data Through a Neural Netwok - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240111840 titled 'Selecting a Tiling Scheme for Processing Instances of Input Data Through a Neural Netwok
Simplified Explanation
The abstract describes an electronic device that uses different tiling schemes to process input data through a neural network. The device selects a tiling scheme based on information about the neural network and processing circuitry, divides the input data into portions according to the selected scheme, processes each portion separately in the neural network, and combines the outputs to generate a final output for the input data.
- The electronic device processes input data through a neural network using various tiling schemes.
- The tiling scheme selected is based on information about the neural network and processing circuitry.
- Input data is divided into portions according to the selected tiling scheme.
- Each portion is processed separately in the neural network.
- The outputs of each portion are combined to generate a final output for the input data.
Potential Applications
This technology could be applied in various fields such as image recognition, natural language processing, and autonomous driving systems.
Problems Solved
This technology helps optimize the processing of input data in neural networks by using different tiling schemes, improving efficiency and accuracy.
Benefits
The use of different tiling schemes allows for more efficient processing of input data, leading to faster and more accurate results in neural network applications.
Potential Commercial Applications
"Optimizing Input Data Processing in Neural Networks for Improved Performance"
Possible Prior Art
There may be prior art related to the use of tiling schemes in neural network processing, but specific examples are not provided in the abstract.
Unanswered Questions
How does the device determine which tiling scheme to use for a specific neural network?
The abstract does not specify the exact criteria or algorithm used by the device to select a tiling scheme.
Are there any limitations to the use of different tiling schemes in processing input data through a neural network?
The abstract does not mention any potential drawbacks or limitations of using different tiling schemes in this context.
Original Abstract Submitted
an electronic device uses a tiling scheme selected from among a set of tiling schemes for processing instances of input data through a neural network. each of the tiling schemes is associated with a different arrangement of portions into which instances of input data are divided for processing in the neural network. in operation, processing circuitry in the electronic device acquires information about a neural network and properties of the processing circuitry. the processing circuitry then selects a given tiling scheme from among a set of tiling schemes based on the information. the processing circuitry next processes instances of input data in the neural network using the given tiling scheme. processing each instance of input data in the neural network includes dividing the instance of input data into portions based on the given tiling scheme, separately processing each of the portions in the neural network, and combining the respective outputs to generate an output for the instance of input data.
