Hierarchical Tree Neural Network for Rapid Data Adaptation
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Solution Overview
Problem
Neural networks are slow to learn and unable to rapidly adapt to changes in small data points, making them inefficient for representing a data universe and requiring extensive retraining.
Innovation Solution
A hierarchical tree-based neural network is developed, where each neuron represents a data point, and the network is adjustable by adding new data points without retraining, with the ability to be flattened into a four-layer neural network for parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional neural networks are trained on big data, then they can represent a comprehensive data universe, but they become slow to learn and unable to rapidly adapt to changes in small data points
Solution Approach 1:
The patent segments the data universe representation into hierarchical tree structures where individual data points can be independently added or modified. Each node in the tree represents a specific data point, allowing selective updates without reprocessing the entire dataset, thus enabling rapid adaptation while maintaining comprehensive coverage.
Solution Approach 2:
The patent implements a dynamic neural network architecture that can be reconfigured in real-time. The system transitions from static trained networks to dynamic structures where neurons can be added, removed, or modified on-the-fly, allowing the network to adapt its structure and behavior rapidly in response to new data points.
2Adaptability or versatility
If additional data points are added to a traditional neural network, then the data universe representation is improved, but extensive retraining is required
Solution Approach 1:
The patent prepares the neural network structure in advance by creating a hierarchical tree framework that anticipates future data additions. The tree structure is pre-configured with nodes and connections that can accommodate new data points without requiring structural reorganization, enabling incremental updates without retraining.
Solution Approach 2:
The patent uses template-based neuron copying where new data points are added by instantiating predefined neuron templates rather than creating entirely new neural pathways. This copying mechanism allows rapid integration of new data while reusing established connection patterns, eliminating the need for extensive retraining.
3Adaptability or versatility
If hierarchical tree processing is used to represent data points, then adaptability improves, but processing time increases compared to parallel processing
Solution Approach 1:
The patent transforms the hierarchical tree structure into a four-layer flattened neural network representation, adding a temporal or processing-dimension to the spatial hierarchy. This dimensional transformation allows the same data to be processed through parallel pathways while maintaining the hierarchical relationships, thus improving processing speed without sacrificing adaptability.
Solution Approach 2:
The patent merges the hierarchical tree structure with parallel processing architectures by combining sequential hierarchical relationships with parallel computation pathways. Multiple tree branches are processed simultaneously through parallel neural network layers, achieving both the adaptability of hierarchical representation and the speed of parallel processing.
Data Source
AI summary
Methods for operating a neural network on processors are provided. Methods may include creating a neural network. The neural network may include a plurality of neurons. Each neuron may represent a data point. Each neuron may be sorted in a hierarchical tree. The sorting may be based on attributes of the data points. The tree may include a plurality of decision forks. Each fork may represent a differentiator between a data point type that categorizes the data points. Methods may receive an additional data point to append to the tree. Methods may receive metadata relating to a categorization of the additional data point. Methods may convert the additional data point to a neuron. Methods may add the neuron to the tree at a bottom edge of the tree. Methods may flatten the tree into a flattened neuron network. Methods may replace the neural network with the flattened neural network.