Interpolating Custom ANN Architectures via Fingerprint Centrality
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Solution Overview
Problem
Current methods for obtaining custom artificial neural network (ANN) architectures are computationally intensive and resource-heavy, making them inefficient and inaccessible for widespread use, especially for consumer-grade computing resources, and often require extensive training and subjective human decisions.
Innovation Solution
An automated process that interpolates among existing ANN architectures to create custom architectures for specific target datasets and performance criteria, using fingerprint calculations and centrality scores to determine interpolation weights, thereby eliminating the need for extensive training and reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to obtain custom ANN architectures through extensive training and human decisions, then architecture performance can be optimized, but computational resources and time requirements become excessively high
Solution Approach 1:
The system pre-calculates fingerprints for multiple existing ANN architectures and stores them in a database before actual custom architecture generation is needed. These pre-computed features enable rapid interpolation-based generation of custom architectures without requiring extensive retraining, thus improving development speed while maintaining performance quality
Solution Approach 2:
Instead of creating entirely new ANN architectures from scratch through resource-intensive training, the system generates custom architectures by interpolating among existing proven architectures. This copying approach leverages previously trained architectures as templates, significantly reducing computational resources and time while producing effective custom architectures
2Measurement precision
If extensive training is performed to obtain custom ANN architectures, then architecture accuracy improves, but computational cost and resource consumption increase significantly
Solution Approach 1:
The system creates custom ANN architectures by interpolating among existing trained architectures rather than performing extensive new training. This copying method achieves high accuracy by combining features from multiple proven architectures while consuming minimal computational energy compared to traditional training approaches
Solution Approach 2:
The system changes the approach from training-based optimization to interpolation-based generation. By computing fingerprints and using centrality scores to determine interpolation weights, the system achieves accurate custom architectures through parameter manipulation rather than energy-intensive training processes
3Adaptability or versatility
If custom ANN architectures are developed for specific target datasets, then performance on those datasets improves, but the complexity of the development process increases
Solution Approach 1:
The system automatically generates custom ANN architectures for target datasets through an automated interpolation process. It computes dataset fingerprints, determines similarities between target and existing datasets, calculates interpolation weights using centrality scores, and generates the custom architecture without requiring subjective human decisions, thus improving adaptability while reducing process complexity
Solution Approach 2:
The system transforms the complex custom architecture development process into a parameter-driven automated procedure. By changing from manual design to algorithmic interpolation based on fingerprint comparisons and centrality measurements, the system achieves dataset-specific optimization with reduced process complexity
4Adaptability or versatility
If traditional ANN architecture generation methods are used, then comprehensive exploration of architecture space is possible, but the time required for generation and training increases substantially
Solution Approach 1:
The system pre-computes and stores fingerprints for multiple existing ANN architectures in a database before custom architecture generation is needed. This preliminary action enables rapid retrieval and interpolation operations, allowing comprehensive architecture exploration without the time penalty of repeated computations
Solution Approach 2:
The system generates custom architectures by copying and interpolating among existing architectures rather than exploring the entire architecture space from scratch. This approach maintains adaptability by selecting from diverse existing architectures while dramatically reducing generation time through the efficiency of interpolation operations
Data Source
AI summary
Methods, systems and apparatuses for a custom artificial neural network (ANN) architecture are disclosed. One method includes selecting existing ANN architectures, calculating ANN architecture fingerprints, calculating ANN architecture pairwise similarities among the existing ANN architectures, calculating centrality scores for the existing ANN architectures using the ANN architecture pairwise similarities, calculating dataset pairwise similarities between the target dataset and each of the existing datasets using dataset fingerprints, calculating target performance scores for the existing ANN architectures on the target dataset using performance scores of the existing ANN architectures on the existing datasets and the dataset pairwise similarities, calculating interpolation weights for the existing ANN architectures using the target performance scores of the existing ANN architectures on the target dataset and the centrality scores, and obtaining the custom ANN architecture by interpolating among the existing ANN architectures using the calculated interpolation weights.