18345083. METHOD AND APPARATUS FOR DISTRIBUTED TRAINING OF ARTIFICIAL INTELLIGENCE MODEL IN CHANNEL-SHARING NETWORK ENVIRONMENT simplified abstract (ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE)
Contents
- 1 METHOD AND APPARATUS FOR DISTRIBUTED TRAINING OF ARTIFICIAL INTELLIGENCE MODEL IN CHANNEL-SHARING NETWORK ENVIRONMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS FOR DISTRIBUTED TRAINING OF ARTIFICIAL INTELLIGENCE MODEL IN CHANNEL-SHARING NETWORK ENVIRONMENT - 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
METHOD AND APPARATUS FOR DISTRIBUTED TRAINING OF ARTIFICIAL INTELLIGENCE MODEL IN CHANNEL-SHARING NETWORK ENVIRONMENT
Organization Name
ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE
Inventor(s)
Myung-Hoon Cha of Daejeon (KR)
METHOD AND APPARATUS FOR DISTRIBUTED TRAINING OF ARTIFICIAL INTELLIGENCE MODEL IN CHANNEL-SHARING NETWORK ENVIRONMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18345083 titled 'METHOD AND APPARATUS FOR DISTRIBUTED TRAINING OF ARTIFICIAL INTELLIGENCE MODEL IN CHANNEL-SHARING NETWORK ENVIRONMENT
Simplified Explanation
The method described in the abstract is for distributed training of an AI model in a channel-sharing network environment. It involves determining whether data parallel processing is applied, calculating computation time and communication time when input data is distributed across multiple computation devices, and unevenly distributing the input data based on these times.
- Explanation of the patent/innovation:
* Determines if data parallel processing is used. * Calculates computation time and communication time for evenly distributed input data. * Unevenly distributes input data based on computation and communication times.
Potential Applications
The technology could be applied in:
- Distributed AI model training
- Network environments with shared channels
Problems Solved
This technology addresses:
- Efficient distribution of input data
- Optimization of computation and communication times
Benefits
The benefits of this technology include:
- Improved training efficiency
- Enhanced performance in channel-sharing networks
Potential Commercial Applications
Optimized distributed training technology can be utilized in:
- Cloud computing services
- AI development platforms
Possible Prior Art
One possible prior art could be:
- Research on distributed training methods in AI models
Unanswered Questions
How does this method compare to existing distributed training techniques in terms of performance and efficiency?
This article does not provide a direct comparison with existing techniques, leaving the reader to wonder about the relative advantages of this method.
What are the specific communication protocols or algorithms used to distribute input data unevenly across multiple computation devices?
The article does not delve into the technical details of the communication protocols or algorithms employed, leaving a gap in understanding the implementation of the method.
Original Abstract Submitted
Disclosed herein is a method for distributed training of an AI model in a channel-sharing network environment. The method includes determining whether data parallel processing is applied, calculating a computation time and a communication time when input data is evenly distributed across multiple computation devices, and unevenly distributing the input data across the multiple computation devices based on the computation time and the communication time.
