Qualcomm incorporated (20240095492). MEMORY MANAGEMENT FOR MATHEMATICAL OPERATIONS IN COMPUTING SYSTEMS WITH HETEROGENEOUS MEMORY ARCHITECTURES simplified abstract
Contents
- 1 MEMORY MANAGEMENT FOR MATHEMATICAL OPERATIONS IN COMPUTING SYSTEMS WITH HETEROGENEOUS MEMORY ARCHITECTURES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MEMORY MANAGEMENT FOR MATHEMATICAL OPERATIONS IN COMPUTING SYSTEMS WITH HETEROGENEOUS MEMORY ARCHITECTURES - 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
MEMORY MANAGEMENT FOR MATHEMATICAL OPERATIONS IN COMPUTING SYSTEMS WITH HETEROGENEOUS MEMORY ARCHITECTURES
Organization Name
Inventor(s)
Jian Shen of San Diego CA (US)
Sameer Wadhwa of San Diego CA (US)
MEMORY MANAGEMENT FOR MATHEMATICAL OPERATIONS IN COMPUTING SYSTEMS WITH HETEROGENEOUS MEMORY ARCHITECTURES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095492 titled 'MEMORY MANAGEMENT FOR MATHEMATICAL OPERATIONS IN COMPUTING SYSTEMS WITH HETEROGENEOUS MEMORY ARCHITECTURES
Simplified Explanation
The present disclosure describes techniques for performing mathematical operations on a processor, specifically for machine learning models. Weight data is initialized in a memory component, input data is stored in another memory component, and operations using the machine learning model are executed based on the weight data and input data.
- Weight data is initialized in a memory component for a machine learning model.
- Input data is stored in a separate memory component.
- Operations using the machine learning model are executed based on the weight data and input data.
- Results of the operations are stored in the memory component with the input data.
Potential Applications
This technology can be applied in various fields such as:
- Autonomous vehicles
- Healthcare diagnostics
- Financial forecasting
Problems Solved
This technology helps in:
- Improving accuracy of predictions
- Enhancing decision-making processes
- Optimizing resource allocation
Benefits
The benefits of this technology include:
- Faster processing of data
- Improved efficiency in machine learning tasks
- Enhanced performance of the processor
Potential Commercial Applications
The potential commercial applications of this technology include:
- Developing advanced AI systems
- Enhancing data analysis tools
- Improving automation processes
Possible Prior Art
One possible prior art for this technology could be:
- Existing machine learning models that perform similar operations on processors
What are the limitations of this technology in real-world applications?
Real-world applications of this technology may face limitations such as:
- Processing power constraints
- Data privacy concerns
How does this technology compare to traditional methods of performing mathematical operations on a processor?
This technology offers advantages such as:
- Enhanced accuracy in predictions
- Improved efficiency in processing data
Original Abstract Submitted
certain aspects of the present disclosure provide techniques and apparatus for performing mathematical operations on a processor. the method generally includes initializing at least a portion of weight data for a machine learning model in a first memory component associated with a processor. input data is stored in a second memory component coupled with the processor. operations using the machine learning model are executed, via a functional unit associated with the processor, based on the at least the portion of the weight data and the input data. a result of the operations using the machine learning model are stored in the second memory component.
