18141917. TENSOR DIMENSION ORDERING TECHNIQUES simplified abstract (NVIDIA Corporation)
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 18141917 titled 'TENSOR DIMENSION ORDERING TECHNIQUES
Simplified Explanation: The patent application describes methods to store tensor operands by sorting them based on performance metrics of tensor operations.
Key Features and Innovation:
- Sorting tensor operands based on performance metrics of tensor operations.
- Enhancing efficiency and performance of tensor operations.
- Optimizing storage and retrieval of tensor data.
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 commonly used.
Problems Solved: This technology addresses the challenges of efficiently storing and accessing tensor operands for complex tensor operations.
Benefits:
- Improved performance and efficiency in tensor operations.
- Enhanced scalability and flexibility in handling large datasets.
- Streamlined storage and retrieval processes for tensor data.
Commercial Applications: Optimizing tensor operand storage can benefit companies involved in machine learning, data analysis, and scientific research by improving the speed and efficiency of tensor operations, leading to better outcomes and cost savings.
Questions about Tensor Operand Storage: 1. How does sorting tensor operands based on performance metrics improve overall efficiency? 2. What are the potential implications of this technology in the field of artificial intelligence and machine learning?
Frequently Updated Research: Stay updated on the latest advancements in tensor operand storage techniques to ensure optimal performance and efficiency in tensor operations.
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.
