View source for Nvidia corporation (20240095302). CHANGING PRECISION OF OPERANDS simplified abstract

=CHANGING PRECISION OF OPERANDS=


==Organization Name==

[[:Category:nvidia corporation|nvidia corporation]]


[[Category:nvidia corporation]]

==Inventor(s)==

[[:Category:Jiqun Tu of New York NY (US)|Jiqun Tu of New York NY (US)]][[Category:Jiqun Tu of New York NY (US)]]

[[:Category:David Maxwell Clark of Mountain View CA (US)|David Maxwell Clark of Mountain View CA (US)]][[Category: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.



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