Qualcomm incorporated (20240112358). DEEP LEARNING MODEL FOR HIGH RESOLUTION PREDICTIONS simplified abstract
Contents
- 1 DEEP LEARNING MODEL FOR HIGH RESOLUTION PREDICTIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEEP LEARNING MODEL FOR HIGH RESOLUTION PREDICTIONS - 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
DEEP LEARNING MODEL FOR HIGH RESOLUTION PREDICTIONS
Organization Name
Inventor(s)
Chieh-Ming Kuo of Taoyuan (TW)
Michel Adib Sarkis of San Diego CA (US)
DEEP LEARNING MODEL FOR HIGH RESOLUTION PREDICTIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112358 titled 'DEEP LEARNING MODEL FOR HIGH RESOLUTION PREDICTIONS
Simplified Explanation
The patent application describes systems and techniques for optimizing deep learning models by generating prediction outputs, determining values outside of a quantization range, clamping the outputs, and generating a single channel output.
- Neural network model used for processing data
- Generation of prediction outputs for multiple output channels
- Detection of values outside quantization range
- Clamping of prediction outputs within range
- Generation of single channel output based on clamped output
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 in improving the accuracy and efficiency of deep learning models by optimizing prediction outputs and ensuring they fall within a quantization range.
Benefits
The benefits of this technology include enhanced model performance, reduced computational resources, and improved overall system reliability.
Potential Commercial Applications
Potential commercial applications of this technology include in industries such as healthcare, finance, retail, and manufacturing for tasks like medical image analysis, fraud detection, customer sentiment analysis, and quality control.
Possible Prior Art
One possible prior art could be techniques for quantization and optimization of neural network models in the field of machine learning.
What are the specific quantization ranges used in this process?
The specific quantization ranges used in this process are not mentioned in the abstract. It would be helpful to know the exact values to understand the level of optimization achieved.
How does clamping the prediction outputs affect the overall model performance?
The impact of clamping the prediction outputs on the overall model performance is not discussed in detail. Understanding the trade-offs between clamping and accuracy would provide valuable insights for potential users of this technology.
Original Abstract Submitted
systems and techniques are provided for deep learning model optimizations. an example process can include generating, based on processing data using a neural network model, a plurality of prediction outputs associated with a plurality of output channels corresponding to a multi-channel prediction target; determining that a prediction output from the plurality of prediction outputs has a value that is outside of a quantization range and one or more remaining prediction outputs from the plurality of prediction outputs have a respective value that is within the quantization range; clamping the prediction output based on the quantization range; and generating a single channel output based on the clamped prediction output and the one or more remaining prediction outputs.
