Resonator Networks for Hyper Vector Factorization
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Solution Overview
Problem
Existing methods for decoding hypervectors representing data structures are resource-intensive, as they require testing every combination of code hypervectors, which is computationally expensive and inefficient.
Innovation Solution
The use of a set of resonator networks, each configured to perform an iterative process to factorize input hypervectors into individual hypervectors representing cognitive concepts, with the networks associated with permutations to efficiently combine and process hypervectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive combination testing is used to decode hypervectors, then decoding accuracy is ensured, but computational complexity and resource consumption increase significantly
Solution Approach 1:
The patent segments the decoding problem by introducing multiple resonator networks (N>1), each responsible for factorizing a specific permuted version of the bundled hypervector. This divides the exhaustive search task into N parallel sub-tasks, where each resonator network independently processes one permutation, significantly reducing the computational burden on each individual network while maintaining overall decoding accuracy through the collective results.
Solution Approach 2:
The patent transforms the single-hypervector decoding problem into an N-dimensional processing space by creating N permuted versions of the bundled hypervector. Each permutation represents a different dimension or viewpoint of the same underlying data structure, allowing the system to explore the solution space from multiple angles simultaneously through N parallel resonator networks, thereby reducing the effective search complexity in each dimension.
2Productivity
If multiple resonator networks process permuted hypervectors in parallel, then processing speed increases, but system complexity and memory requirements increase
Solution Approach 1:
The patent merges N separate processing tasks (factorizing N different permuted hypervectors) into a single bundled hypervector that contains all the necessary information. Instead of maintaining N completely independent systems, the invention combines the input data into one structure that can be permuted and distributed to N resonator networks, reducing memory redundancy and simplifying the overall system architecture while enabling parallel processing.
Solution Approach 2:
The bundled hypervector structure serves multiple functions simultaneously: it acts as the input for N different resonator networks, contains N permuted versions of the original data, and enables both parallel processing and sequential processing modes. This multi-functional design allows the same data structure to support various processing strategies without requiring separate specialized structures for each mode.
3Measurement precision
If sequential processing with subtraction is used to reduce noise, then processing accuracy improves, but processing time increases
Solution Approach 1:
The patent introduces dynamic processing modes that can adapt between parallel and sequential execution based on the specific requirements of the decoding task. The system can dynamically switch between exploiting additive superpositions (parallel mode for speed) and multiplicative superpositions with subtraction (sequential mode for noise reduction), allowing optimal performance characteristics to be selected or combined based on the input data properties and desired output quality.
Data Source
AI summary
The present disclosure relates to a resonator network system comprising a set of resonator networks, each resonator network being configured to execute a resonator network, the resonator network being configured to receive an input hypervector representing a data structure and to perform an iterative process in order to factorize the input hypervector into individual hypervectors representing a set of concepts respectively, the set of N resonator networks being associated with N permutations respectively. The resonator network system being configured for applying the N permutations to N first hypervectors respectively, the N first hypervectors representing a set of N data structures respectively; and combining the N permuted hypervectors into a bundled hypervector. The resonator networks being configured for processing the bundled hypervector respectively, thereby factorizing the first hypervectors.