Nvidia corporation (20240095302). CHANGING PRECISION OF OPERANDS simplified abstract
Contents
- 1 CHANGING PRECISION OF OPERANDS
CHANGING PRECISION OF OPERANDS
Organization Name
Inventor(s)
David Maxwell Clark of Mountain View CA (US)
CHANGING PRECISION OF OPERANDS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095302 titled 'CHANGING PRECISION OF OPERANDS
Simplified Explanation
The patent application describes apparatuses, systems, and techniques for performing matrix multiply-accumulate (MMA) operations using MMA instructions for data of a second type, such as converting 32-bit floating point (FP32) data values to TensorFloat-32 (TF32) input operands to compute FP32 outputs.
- MMA operations are performed on data of a first type using MMA instructions for data of a second type
- TF32 MMA instruction computes FP32 output using TF32 input operands converted from FP32 data values
Potential Applications
The technology can be applied in:
- High-performance computing
- Artificial intelligence and machine learning
- Scientific simulations
Problems Solved
The technology addresses issues related to:
- Efficient computation of matrix operations
- Precision and accuracy in floating-point calculations
- Accelerating data processing tasks
Benefits
The benefits of this technology include:
- Improved performance and speed in matrix operations
- Enhanced accuracy and precision in floating-point calculations
- Increased efficiency in data processing tasks
Potential Commercial Applications
This technology has potential commercial applications in:
- Data centers and cloud computing
- Graphics processing units (GPUs) and accelerators
- Supercomputing and high-performance computing clusters
Possible Prior Art
One possible prior art could be the use of specialized hardware accelerators for matrix operations in high-performance computing environments.
Unanswered Questions
How does this technology compare to existing methods for matrix operations in terms of speed and accuracy?
This technology aims to improve the speed and accuracy of matrix operations by using TF32 MMA instructions. However, a direct comparison with existing methods is not provided in the abstract.
What impact could this technology have on the energy efficiency of data processing tasks?
The abstract mentions the efficiency of data processing tasks, but it does not specifically address the potential impact on energy consumption or energy efficiency.
Original Abstract Submitted
apparatuses, systems, and techniques to perform matrix multiply-accumulate (mma) operations on data of a first type using one or more mma instructions for data of a second type. in at least one embodiment, a single tensorfloat-32 (tf32) mma instruction computes a 32-bit floating point (fp32) output using tf32 input operands converted from fp32 data values.
