Point Grid Network With Learnable Graph-to-Grid Transformation
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Solution Overview
Problem
Existing graph convolutional networks (GCNs) face performance limitations and irregular workloads when processing graph-structured data, hindering efficient execution on AI processors due to the need for irregular sparse tensor operations.
Innovation Solution
A point grid network with an auto grid module that transforms graph-structured data to grid-structured data using a learnable assignment matrix, enabling regular tensor operations and preserving intrinsic relationships, thereby improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If graph convolutional networks use irregular sparse tensor operations to process graph-structured data, then they can preserve the intrinsic relationships of graph data, but they face performance limitations and cannot be efficiently executed on AI processors
Solution Approach 1:
The patent introduces an auto grid module as an intermediary component that transforms graph-structured data into grid-structured data before processing. This mediator converts the irregular graph structure into a regular grid format that AI processors can efficiently handle, while the learnable assignment matrix ensures the transformation preserves essential graph relationships. The intermediary resolves the contradiction by enabling efficient processor execution without completely losing the structural information.
Solution Approach 2:
The patent changes the structural parameters of the data representation from irregular graph format to regular grid format through the learnable assignment matrix. By dynamically adjusting the assignment parameters during training, the system transforms the data structure to be compatible with standard AI processor architectures while maintaining the ability to represent graph relationships through the learned assignment patterns.
2Loss of information
If graph convolutional networks process data in graph-structured format, then they maintain data integrity and relationships, but they incur high computational complexity and irregular workloads
Solution Approach 1:
The auto grid module serves as an intermediary transformation layer that converts graph data to grid data using a learnable assignment matrix. This transformation preserves information by learning optimal assignment patterns that maintain graph relationships, while the resulting grid structure simplifies subsequent computational operations and reduces overall system complexity.
Solution Approach 2:
The patent creates a transformed copy of the graph data in grid format through the assignment matrix. This copy maintains the essential relationships and information from the original graph structure but represents them in a simplified grid format that is easier and less complex to process computationally.
3Ease of manufacture
If existing GCNs use fixed transformation methods for graph data, then the processing pipeline is simple, but they cannot adapt to different graph structures and achieve limited performance
Solution Approach 1:
The patent replaces fixed transformation methods with a dynamic learnable assignment matrix that adapts to different graph structures during training. The assignment matrix parameters are learned automatically from data, allowing the transformation pipeline to dynamically adjust its behavior to suit various graph types while maintaining a relatively simple overall processing architecture.
Solution Approach 2:
The system performs self-adaptation through the learnable assignment matrix that automatically learns optimal transformation patterns from the input graph data. The network self-adjusts its transformation strategy based on the specific graph structure it encounters, eliminating the need for manual configuration or complex predefined pipelines for different graph types.
Data Source
AI summary
A point grid network is a neural network that can model graph-structured data. The point grid network receives a graph-structured data sample, which may be a graph representation of an object. The point grid network uses an assignment matrix to transform the graph representation into a grid representation of the object. The assignment matrix defines whether graph nodes in the graph representation is to be assigned to grid elements in the grid structure. The grid representation is a tensor that can be processed through convolutional operations or other types of tensor operations. The point grid network can perform convolution on the grid representation and one or more filters to generate a grid-structured feature map. Values in the filter (s) and values in the assignment matrix are determined through training the point grid network. The point grid network may further determine a condition of the object based on the grid-structured feature map.