Nvidia corporation (20240338175). TENSOR DIMENSION ORDERING TECHNIQUES simplified abstract
Contents
TENSOR DIMENSION ORDERING TECHNIQUES
Organization Name
Inventor(s)
Paul Martin Springer of Iserlohn (DE)
Ali Mohamad Charara of Knoxville TN (US)
Markus Hoehnerbach of Santa Clara CA (US)
Andreas Roland Hehn of Zürich (CH)
TENSOR DIMENSION ORDERING TECHNIQUES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338175 titled 'TENSOR DIMENSION ORDERING TECHNIQUES
Simplified Explanation: The patent application describes apparatuses, systems, and techniques for storing tensor operands, where the modes of tensor operands are sorted based on performance metrics of tensor operations to be performed.
- Key Features and Innovation:
- Sorting tensor operands based on performance metrics of tensor operations
- Efficient storage and retrieval of tensor data
- Optimization of tensor operations based on performance criteria
Potential Applications: This technology can be applied in various fields such as machine learning, artificial intelligence, data analytics, and scientific computing where tensor operations are common.
Problems Solved: This technology addresses the challenges of efficiently storing and managing tensor data, optimizing tensor operations for performance, and enhancing overall computational efficiency.
Benefits:
- Improved performance of tensor operations
- Enhanced efficiency in storing and retrieving tensor data
- Optimization of computational resources for tensor processing
Commercial Applications: The technology can be utilized in industries such as healthcare for medical imaging analysis, finance for risk assessment models, and manufacturing for process optimization using machine learning algorithms.
Frequently Updated Research: Researchers are constantly exploring new algorithms and techniques to further optimize tensor operations and enhance the performance of tensor processing systems.
Questions about Tensor Operand Storage: 1. How does sorting tensor operands based on performance metrics improve computational efficiency? 2. What are the potential challenges in implementing this technology in real-world applications?
By providing a detailed answer to these questions, readers can gain a deeper understanding of the innovation behind tensor operand storage and its implications in various industries.
Original Abstract Submitted
apparatuses, systems, and techniques to store tensor operands. in at least one embodiment, modes of one or more tensor operands are sorted based, at least in part, on one or more performance metrics of one or more tensor operations to be performed using said one or more tensor operands.
