Nvidia corporation (20240161224). APPLICATION PROGRAMMING INTERFACE TO GENERATE A TENSOR ACCORDING TO A TENSOR MAP simplified abstract

APPLICATION PROGRAMMING INTERFACE TO GENERATE A TENSOR ACCORDING TO A TENSOR MAP

Organization Name

nvidia corporation

Inventor(s)

Harold Carter Edwards of Campbell CA (US)

Stephen Anthony Bernard Jones of San Francisco CA (US)

Alexander Lev Minkin of Los Altos CA (US)

Olivier Giroux of Santa Clara CA (US)

Gokul Ramaswamy Hirisave Chandra Shekhara of Bangalore (IN)

Vishalkumar Ketankumar Mehta of Stäfa (CH)

Aditya Avinash Atluri of Redmond WA (US)

Apoorv Parle of Santa Clara CA (US)

Chao Li of Austin TX (US)

Ronny Meir Krashinsky of Portola Valley CA (US)

Alan Kaatz of Seattle WA (US)

Andrew Robert Kerr of Atlanta GA (US)

Jack H. Choquette of Palo Alto CA (US)

APPLICATION PROGRAMMING INTERFACE TO GENERATE A TENSOR ACCORDING TO A TENSOR MAP - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240161224 titled 'APPLICATION PROGRAMMING INTERFACE TO GENERATE A TENSOR ACCORDING TO A TENSOR MAP

Simplified Explanation

The patent application describes a method and system for translating a first tensor into a second tensor according to a tensor map without storing information about memory transactions. This is achieved through the use of circuits that perform an application programming interface (API) to facilitate the translation process.

• Explanation of the patent/innovation:

- Apparatuses, systems, and techniques for translating tensors without storing memory transaction information - Circuits performing an API to translate tensors according to a tensor map - Elimination of the need to store memory transaction data during translation process

Potential Applications

This technology could be applied in various fields such as: - Machine learning - Image processing - Data analysis

Problems Solved

- Efficient tensor translation without memory transaction storage - Streamlining the translation process - Reducing memory usage during tensor operations

Benefits

- Improved performance in tensor operations - Simplified translation process - Reduced memory overhead

Potential Commercial Applications

"Tensor Translation System for Memory-Efficient Operations" could be used in industries such as: - Artificial intelligence - Robotics - Big data analytics

Possible Prior Art

There may be prior art related to tensor operations and memory efficiency in computational systems, but specific examples are not provided in the abstract.

How does this technology compare to existing methods of tensor translation in terms of efficiency and memory usage?

This article does not provide a direct comparison with existing methods, leaving the reader to wonder about the advantages of this technology over current practices.

Are there any limitations to the application of this technology in certain types of tensor operations or data structures?

The abstract does not address any potential limitations or constraints that may arise when implementing this technology, leaving room for further exploration into its practicality in different scenarios.

Original Abstract Submitted

apparatuses, systems, and techniques to cause a first tensor to be translated into a second tensor according to a tensor map without storing information about a memory transaction corresponding to the translation. in at least one embodiment, one or more circuits are to perform an application programming interface (api) to cause a first tensor to be translated into a second tensor according to a tensor map without storing information about one or more memory transactions corresponding to the translation.