Fujifilm corporation (20240311663). INFERENCE DEVICE simplified abstract
Contents
- 1 INFERENCE DEVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INFERENCE DEVICE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Inference Devices
- 1.11 Original Abstract Submitted
INFERENCE DEVICE
Organization Name
Inventor(s)
Seiji Tanaka of Saitama-shi (JP)
INFERENCE DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240311663 titled 'INFERENCE DEVICE
Simplified Explanation
The patent application describes an inference device with two arithmetic modules that perform arithmetic processing, including convolution and pooling processes, on image data with different numbers of channels.
Key Features and Innovation
- First arithmetic module with a memory for storing row data items from the first image data and arithmetic units for executing a convolution process.
- Second arithmetic module with a memory for storing row data items from the second image data and arithmetic units for executing a convolution process.
- Different numbers of channels in the first and second image data.
Potential Applications
This technology can be applied in image processing, pattern recognition, and machine learning tasks that require convolution and pooling operations on multi-channel image data.
Problems Solved
- Efficient processing of image data with varying numbers of channels.
- Streamlined convolution and pooling processes for improved inference accuracy.
Benefits
- Enhanced performance in image recognition tasks.
- Increased efficiency in processing multi-channel image data.
- Versatile application in various machine learning algorithms.
Commercial Applications
Convolutional Neural Networks (CNNs)
This technology can be utilized in CNNs for tasks such as image classification, object detection, and facial recognition, enhancing the accuracy and speed of inference processes.
Questions about Inference Devices
How does the inference device handle image data with different numbers of channels?
The inference device uses separate arithmetic modules for processing image data with varying channel numbers, ensuring efficient convolution and pooling operations.
What are the potential advantages of using an inference device with multiple arithmetic modules?
Having multiple arithmetic modules allows for parallel processing of image data, leading to faster inference speeds and improved performance in machine learning tasks.
Original Abstract Submitted
an inference device includes a first arithmetic module and a second arithmetic module that execute arithmetic processing including a convolution process and a pooling process. the first arithmetic module includes a first memory that stores a plurality of first row data items generated by dividing first image data for each first number of pixels in a row direction and a plurality of first arithmetic units that execute a first convolution process on the plurality of first row data items. the second arithmetic module includes a second memory that stores a plurality of second row data items generated by dividing second image data for each second number of pixels in the row direction and a plurality of second arithmetic units that execute a second convolution process on the plurality of second row data items. the first image data and the second image data have different numbers of channels.
