Samsung electronics co., ltd. (20240135147). METHOD AND APPARATUS WITH TRANSFORMER MODEL TRAINING simplified abstract
Contents
- 1 METHOD AND APPARATUS WITH TRANSFORMER MODEL TRAINING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS WITH TRANSFORMER MODEL TRAINING - 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 WITH TRANSFORMER MODEL TRAINING
Organization Name
Inventor(s)
METHOD AND APPARATUS WITH TRANSFORMER MODEL TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135147 titled 'METHOD AND APPARATUS WITH TRANSFORMER MODEL TRAINING
Simplified Explanation
The patent application describes a device and method for training a transformer model by optimizing the allocation of resources to different layers in the model.
- The processors are configured to identify batches of training data and split them into micro-batches.
- Layer pairs are selected for each micro-batch and a processing order is determined.
- Resource information is calculated for each layer pair.
- Resources are allocated to the layer pairs based on the processing order and resource information.
Potential Applications
This technology can be applied in various fields such as natural language processing, machine translation, and image recognition where transformer models are used for training and inference.
Problems Solved
This technology addresses the challenge of efficiently training transformer models by optimizing the allocation of resources to different layers, leading to improved performance and faster training times.
Benefits
The optimized allocation of resources ensures that each layer in the transformer model receives the necessary resources for training, resulting in better model accuracy and efficiency.
Potential Commercial Applications
This technology can be valuable for companies developing AI-powered products and services that rely on transformer models, such as chatbots, recommendation systems, and automated content generation.
Possible Prior Art
One possible prior art could be the use of parallel processing techniques in training neural networks to optimize resource allocation and improve training efficiency.
Unanswered Questions
How does this technology compare to existing methods for resource allocation in transformer models?
This article does not provide a direct comparison with existing methods for resource allocation in transformer models. Further research or a comparative study would be needed to evaluate the effectiveness of this technology in comparison to other approaches.
What impact could this technology have on the scalability of transformer models for large datasets?
The article does not address the scalability of transformer models for large datasets. It would be interesting to investigate how this technology could potentially improve the scalability of transformer models and handle larger datasets more efficiently.
Original Abstract Submitted
a device including processors configured to execute instructions and memories storing the instructions, which when executed by the processors configure the processors to perform an operation for training a transformer model having a plurality of encoders and a plurality of decoders by configuring the processors to identify the batches of training data into a plurality of micro-batches, select layer pairs for the plurality of micro-batches, assemble a processing order of the layer pairs, determining resource information to be allocated to the layer pairs, and allocate resources to the layer pairs based on the determined resource information to be allocated to the layer pairs, dependent con the processing order of the layer pairs.
