17934178. MEMORY MANAGEMENT FOR MATHEMATICAL OPERATIONS IN COMPUTING SYSTEMS WITH HETEROGENEOUS MEMORY ARCHITECTURES simplified abstract (QUALCOMM Incorporated)
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 17934178 titled 'MEMORY MANAGEMENT FOR MATHEMATICAL OPERATIONS IN COMPUTING SYSTEMS WITH HETEROGENEOUS MEMORY ARCHITECTURES
Simplified Explanation
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.
- Techniques and apparatus for performing mathematical operations on a processor
- Initialization of weight data for a machine learning model in a first memory component
- Storage of input data in a second memory component
- Execution of operations using the machine learning model via a functional unit associated with the processor
- Storage of the result of the operations in the second memory component
Potential Applications
This technology can be applied in various fields such as:
- Artificial intelligence
- Data analysis
- Pattern recognition
Problems Solved
This technology helps in:
- Efficient execution of mathematical operations
- Improving machine learning model performance
- Streamlining data processing tasks
Benefits
The benefits of this technology include:
- Faster processing speed
- Enhanced accuracy in mathematical operations
- Optimal utilization of memory components
Potential Commercial Applications
The potential commercial applications of this technology include:
- Development of advanced machine learning systems
- Integration into data processing software
- Implementation in hardware for optimized performance
Possible Prior Art
One possible prior art for this technology could be the use of specialized processors for machine learning tasks in the past.
What are the specific mathematical operations performed by the processor in this technology?
The specific mathematical operations performed by the processor in this technology include executing operations using the machine learning model based on weight data and input data.
How does this technology improve the efficiency of machine learning models?
This technology improves the efficiency of machine learning models by optimizing the execution of operations and enhancing the performance of mathematical calculations.
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.
