Neural Network Layer Parallelization Using Metadata Matching
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Solution Overview
Problem
Existing neural network models face challenges in efficiently converging to results during training and inference operations due to the lack of effective parallelization strategies, particularly in large and diverse applications, which require significant time and effort to establish.
Innovation Solution
A parallel processing apparatus and method that generates optimized parallelization strategies for neural networks by comparing metadata of target layers with reference information in a database, allowing for real-time adaptation and continuous updating of strategies to accommodate new and diverse applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional sequential processing is used for neural network training and inference, then implementation simplicity is maintained, but training time and response latency become excessively long
Solution Approach 1:
The patent divides the neural network into multiple layers and segments processing operations into parallel tasks. Each layer or group of layers is processed independently through parallel computation units, allowing simultaneous execution of multiple operations that were previously performed sequentially, thereby reducing training time and inference latency.
Solution Approach 2:
The patent implements dynamic parallelization strategies that adapt to different neural network architectures and workloads. The system dynamically generates and updates parallelization strategies based on metadata from reference databases, allowing the processing architecture to flexibly adjust to various application requirements rather than using a fixed sequential approach.
2Productivity
If parallelization strategies are manually established for each application, then processing efficiency is improved, but the time and effort required to establish strategies becomes prohibitive
Solution Approach 1:
The patent pre-establishes parallelization strategies for reference neural networks and stores them in a reference database. When a new neural network requires processing, the system retrieves and adapts pre-existing strategies from the database based on metadata matching, eliminating the need to manually create parallelization strategies from scratch for each new application.
Solution Approach 2:
The patent copies proven parallelization strategies from reference neural networks to target neural networks. By matching metadata between reference and target networks, the system identifies suitable reference strategies and adapts them for the target network, significantly reducing the time and effort required to establish parallelization strategies while maintaining processing efficiency.
3Adaptability or versatility
If fixed parallelization strategies are used, then implementation simplicity is maintained, but adaptability to new and diverse applications is reduced
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously updates the reference database with new parallelization strategies based on performance data and metadata from processed neural networks. This feedback loop enables the system to learn from experience and improve its strategy generation capabilities, enhancing adaptability to new and diverse applications while managing complexity through systematic knowledge accumulation.
Solution Approach 2:
The patent changes key parameters by maintaining a dynamic reference database that stores metadata and parallelization strategies for multiple reference neural networks. The system queries and adapts strategies based on parameter matching between reference and target networks, allowing flexible adaptation to diverse applications without requiring completely new strategies for each case.
Data Source
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AI summary
A parallel processing method and apparatus for a neural network model. The parallel processing method includes extracting metadata of a target layer included in a target model, measuring a similarity between the target layer and each of reference layers by comparing the metadata of the target layer to reference metadata of each of the reference layers, selecting a corresponding layer among the reference layers based on the similarities, and generating a parallelization strategy for the target layer based on a reference parallelization strategy matching the corresponding layer.