18219622. TENSOR MAP CACHE STORAGE simplified abstract (NVIDIA Corporation)
Contents
- 1 TENSOR MAP CACHE STORAGE
TENSOR MAP CACHE STORAGE
Organization Name
Inventor(s)
Gokul Ramaswamy Hirisave Chandra Shekhara of Bangalore (IN)
Alexander Lev Minkin of Los Altos CA (US)
Harold Carter Edwards of Campbell CA (US)
Yashwardhan Narawane of San Jose CA (US)
TENSOR MAP CACHE STORAGE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18219622 titled 'TENSOR MAP CACHE STORAGE
Simplified Explanation
The patent application describes apparatuses, systems, and techniques for storing one or more tensor maps in cache storages using tensor acceleration logic circuits in a processor.
- Tensor maps are stored in cache storages using tensor acceleration logic circuits.
- The processor is equipped with one or more tensor acceleration logic circuits.
- The technology aims to optimize the storage and retrieval of tensor maps for efficient processing.
Potential Applications
This technology can be applied in various fields such as machine learning, artificial intelligence, image processing, and data analytics where tensor operations are commonly used.
Problems Solved
1. Efficient storage and retrieval of tensor maps. 2. Accelerated processing of tensor operations.
Benefits
1. Improved performance in processing tensor operations. 2. Reduced latency in accessing tensor maps. 3. Enhanced efficiency in handling complex data structures.
Potential Commercial Applications
Optimizing tensor operations in machine learning algorithms. Enhancing image processing applications with faster retrieval of tensor maps.
Possible Prior Art
One possible prior art could be the use of specialized hardware accelerators for tensor operations in machine learning applications.
What are the potential scalability challenges of implementing this technology in large-scale systems?
Scalability challenges may arise in managing the cache storages and ensuring efficient distribution of tensor maps across multiple processors in large-scale systems. Additionally, coordinating the synchronization of tensor acceleration logic circuits in a distributed environment could pose challenges.
How does this technology compare to existing solutions for storing and processing tensor maps efficiently?
This technology offers a more integrated approach by incorporating tensor acceleration logic circuits within the processor itself, potentially reducing the need for external hardware accelerators. It aims to provide a more streamlined and optimized solution for storing and processing tensor maps efficiently.
Original Abstract Submitted
Apparatuses, systems, and techniques to store one or more tensor maps in one or more cache storages. In at least one embodiment, a processor includes one or more tensor acceleration logic circuits to cause one or more tensor maps to be stored in one or more cache storages.
