Heterogeneous Kernel DSP for Irregular Graph Data
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Solution Overview
Problem
Traditional CNN models are ineffective in providing accurate predictive results for data modeled by irregular grids, which are common in real-world applications such as social networks, telecommunication networks, and disease prediction, due to the lack of local stationarity and compositionality in irregular grid data.
Innovation Solution
A digital signal processor (DSP) with a self-learning/self-trainable feature is developed, comprising a series of cascaded hidden layers with heterogeneous kernels, optimized using known graph data, to generate predictive results for irregular grid datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CNN models are used for irregular grid data, then the model structure is simple and easy to implement, but the classification accuracy is low and predictive results are ineffective
Solution Approach 1:
The patent applies local quality by making each kernel in the neural network heterogeneous with specialized functions. Different kernels are designed to handle specific types of graph operations (e.g., node feature aggregation, edge relationship modeling, spatial transformations) rather than using uniform convolution operations. This allows the model to adapt to local variations in graph structure and data characteristics, significantly improving classification accuracy on irregular grid data while maintaining a manageable architecture through functional specialization.
2Reliability
If heterogeneous kernels with multiple filters are used in each layer, then the predictive capability for irregular grid data is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the computational workload by dividing the graph data processing into distinct hierarchical layers, where each layer handles specific computational tasks. The heterogeneous kernels are organized in sequences where early layers perform feature extraction and later layers perform higher-level reasoning. This segmentation allows parallel processing of different kernel operations and enables selective computation based on data characteristics, improving predictive capability while managing computational complexity through structured task division.
Solution Approach 2:
The patent implements partial action by applying heterogeneous kernels selectively based on the specific requirements of each graph data type and processing stage. Not all kernels are activated simultaneously or applied to all data; instead, the model dynamically selects and applies only the necessary kernel operations for each computation step. This reduces unnecessary computational overhead while maintaining high predictive capability for the specific irregular grid data being processed.
3Adaptability or versatility
If self-learning/training feature is implemented, then the model adapts to specific graph data characteristics, but the training time and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-defining the architecture and functional characteristics of heterogeneous kernels before training begins. The kernel structures, filter configurations, and connection patterns are designed in advance based on theoretical considerations and benchmark analyses. During training, only the parameters and weights need to be optimized rather than the entire architecture, which significantly reduces training time while maintaining strong adaptability to specific graph data characteristics through the pre-configured heterogeneous kernel framework.
Data Source
AI summary
Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for generating, training, and utilizing a digital signal processor (DSP) to evaluate graph data that may include irregular grid graph data. An example DSP that may be generated, trained, and used may include a set of hidden layers, wherein each hidden layer of the set of hidden layers comprises a set of heterogeneous kernels (HKs), and wherein each HK of the set of HKs includes a corresponding set of filters selected from the constructed set of filters and associated with one or more initial Laplacian operators and corresponding initial filter parameters.