18149248. MANAGEMENT OF ARTIFICIAL INTELLIGENCE RESOURCES IN A DISTRIBUTED RESOURCE ENVIRONMENT simplified abstract (NVIDIA Corporation)
Contents
MANAGEMENT OF ARTIFICIAL INTELLIGENCE RESOURCES IN A DISTRIBUTED RESOURCE ENVIRONMENT
Organization Name
Inventor(s)
Pritish Nahar of Sunnyvale CA (US)
MANAGEMENT OF ARTIFICIAL INTELLIGENCE RESOURCES IN A DISTRIBUTED RESOURCE ENVIRONMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18149248 titled 'MANAGEMENT OF ARTIFICIAL INTELLIGENCE RESOURCES IN A DISTRIBUTED RESOURCE ENVIRONMENT
The abstract of the patent application describes approaches for managing AI-related resources in a distributed environment to support accelerated machine learning applications for different users. An AI manager can determine the requirements, capabilities, and limitations of various AI components and optimize resource selection and configuration for factors like throughput and inference latency.
- Simplified Explanation:
The patent application discusses managing AI-related resources in a distributed environment to support machine learning applications efficiently.
- Key Features and Innovation:
- Utilizing an AI manager to determine requirements and capabilities of AI components - Optimizing resource selection and configuration for factors like throughput and latency
- Potential Applications:
- Accelerated machine learning applications - Distributed AI resource management systems
- Problems Solved:
- Efficient management of AI-related resources - Optimization of resource selection for machine learning applications
- Benefits:
- Improved performance of machine learning applications - Enhanced resource utilization and throughput
- Commercial Applications:
Optimizing AI resource management for various industries such as healthcare, finance, and e-commerce to improve machine learning applications.
- Questions about AI:
1. How does the AI manager optimize resource selection for machine learning applications? - The AI manager determines the requirements and capabilities of AI components to select and configure resources efficiently.
2. What are the potential benefits of using an AI manager for managing AI-related resources? - The benefits include improved performance of machine learning applications and enhanced resource utilization.
Original Abstract Submitted
Approaches presented herein provide for the management of artificial intelligence (AI)-related resources in a distributed resource environment, such as may be used to support accelerated machine learning (ML) applications on behalf of different users. Management functionality can be provided using an AI manager, such as a management service, that can determine the requirements, capabilities, and limitations of various available AI-related components, such as those of a plurality of AI models, engines, and accelerators, as well as the hardware (e.g., graphics processing units (GPUs)) that run or make up these AI-related resources. An AI manager can determine a selection and configuration of resources that is not only appropriate for use with a specific AI model, but that can also be optimized for factors such as throughput, resource utilization, and inference latency. An AI manager can ensure compatibility of resources and configuration, and can enforce access control to models and data.
