Qualcomm incorporated (20240119721). PROCESSING DATA USING CONVOLUTION AS A TRANSFORMER OPERATION simplified abstract
Contents
- 1 PROCESSING DATA USING CONVOLUTION AS A TRANSFORMER OPERATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PROCESSING DATA USING CONVOLUTION AS A TRANSFORMER OPERATION - 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
PROCESSING DATA USING CONVOLUTION AS A TRANSFORMER OPERATION
Organization Name
Inventor(s)
Dharma Raj Kc of Tucson AZ (US)
Venkata Ravi Kiran Dayana of San Diego CA (US)
Meng-Lin Wu of San Diego CA (US)
Venkateswara Rao Cherukuri of San Diego CA (US)
PROCESSING DATA USING CONVOLUTION AS A TRANSFORMER OPERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119721 titled 'PROCESSING DATA USING CONVOLUTION AS A TRANSFORMER OPERATION
Simplified Explanation
The patent application describes a method for processing image data using convolution operations in a machine learning system.
- Receiving a first set of features associated with an image in a three-dimensional shape.
- Applying a depth-wise separable convolutional filter to the first set of features to generate a first output.
- Applying a pointwise convolutional filter to the first output to generate a second output based on global information from the image.
- Modifying the second output to the three-dimensional shape to generate a second set of features.
- Combining the first set of features and the second set of features to generate an output set of features.
Potential Applications
This technology can be applied in image recognition, object detection, and other computer vision tasks.
Problems Solved
This technology helps in extracting relevant features from image data efficiently and accurately.
Benefits
The method improves the performance of machine learning systems in processing image data by utilizing convolution operations effectively.
Potential Commercial Applications
- Enhanced image recognition systems
- Improved object detection algorithms
Possible Prior Art
Prior art may include existing methods for convolutional operations in machine learning systems, such as traditional convolutional neural networks.
Unanswered Questions
How does this method compare to other convolutional techniques in terms of computational efficiency?
The patent application does not provide a direct comparison with other convolutional techniques in terms of computational efficiency.
What are the potential limitations or drawbacks of this method in real-world applications?
The patent application does not discuss any potential limitations or drawbacks of the described method in real-world applications.
Original Abstract Submitted
systems and techniques are described herein for processing data (e.g., image data) using convolution as a transformer (cat) operations. the method includes receiving, at a convolution engine of a machine learning system, a first set of features, the first set of features being associated with an image and having a three-dimensional shape, applying, via the convolution engine, a depth-wise separable convolutional filter to the first set of features to generate a first output, applying, via the convolution engine, a pointwise convolutional filter to the first output to generate a second output based on global information from a spatial dimension and a channel dimension associated with the image, modifying the second output to the three-dimensional shape to generate a second set of features and combining the first set of features and the second set of features to generate an output set of features.
