Intel corporation (20240119271). METHODS, SYSTEMS, ARTICLES OF MANUFACTURE AND APPARATUS TO MAP WORKLOADS simplified abstract
Contents
- 1 METHODS, SYSTEMS, ARTICLES OF MANUFACTURE AND APPARATUS TO MAP WORKLOADS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS, SYSTEMS, ARTICLES OF MANUFACTURE AND APPARATUS TO MAP WORKLOADS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHODS, SYSTEMS, ARTICLES OF MANUFACTURE AND APPARATUS TO MAP WORKLOADS
Organization Name
Inventor(s)
Amit Bleiweiss of Yad Binyamin (IL)
Eliran Zimmerman of Maalot (IL)
METHODS, SYSTEMS, ARTICLES OF MANUFACTURE AND APPARATUS TO MAP WORKLOADS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119271 titled 'METHODS, SYSTEMS, ARTICLES OF MANUFACTURE AND APPARATUS TO MAP WORKLOADS
Simplified Explanation
The patent application describes methods and systems for mapping workloads in a neural network by defining performance characteristic targets, applying resource configurations, calculating results metrics, and generating resource mapping files based on the metrics.
- The apparatus includes a constraint definer, an action determiner, a reward determiner, and a layer map generator.
- The constraint definer sets performance characteristic targets for the neural network.
- The action determiner applies resource configurations to candidate resources.
- The reward determiner calculates a results metric based on resource performance metrics and performance characteristic targets.
- The layer map generator generates a resource mapping file with resource assignments for corresponding layers of the neural network based on the results metric.
Potential Applications
This technology could be applied in optimizing the performance of neural networks in various industries such as healthcare, finance, and autonomous vehicles.
Problems Solved
This technology solves the problem of efficiently mapping workloads in neural networks to improve performance and resource utilization.
Benefits
The benefits of this technology include enhanced neural network performance, optimized resource allocation, and improved overall efficiency in computing tasks.
Potential Commercial Applications
A potential commercial application of this technology could be in cloud computing services, where optimizing neural network performance is crucial for various applications.
Possible Prior Art
One possible prior art could be the use of machine learning algorithms to optimize resource allocation in neural networks, but the specific method described in this patent application appears to be novel and inventive.
Unanswered Questions
How does this technology compare to existing methods for mapping workloads in neural networks?
This article does not provide a direct comparison to existing methods for mapping workloads in neural networks.
What are the specific industries or use cases where this technology could have the most significant impact?
The article does not specify the specific industries or use cases where this technology could have the most significant impact.
Original Abstract Submitted
methods, apparatus, systems and articles of manufacture are disclosed to map workloads. an example apparatus includes a constraint definer to define performance characteristic targets of the neural network, an action determiner to apply a first resource configuration to candidate resources corresponding to the neural network, a reward determiner to calculate a results metric based on (a) resource performance metrics and (b) the performance characteristic targets, and a layer map generator to generate a resource mapping file, the mapping file including respective resource assignments for respective corresponding layers of the neural network, the resource assignments selected based on the results metric.
