Recurrent Binary Embedding for GPU Information Retrieval
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Solution Overview
Problem
Current information retrieval systems face challenges in efficiently processing large numbers of candidate answers due to high computational burdens, particularly when using semantic embeddings, which can exceed the memory capacity of GPUs and result in increased latency and power consumption.
Innovation Solution
The implementation of a Recurrent Binary Embedding (RBE) model for GPU-enabled information retrieval, which transforms candidate answers into compact binary vectors, enabling efficient storage and mathematical operations, and utilizes a single-instruction, multiple-thread architecture for exhaustive similarity computations, allowing for real-time retrieval of relevant answers among billions of candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic embeddings are used for information retrieval, then retrieval accuracy is improved, but computational burden increases
Solution Approach 1:
The patent extracts only the essential semantic features by transforming high-dimensional semantic embeddings into compact binary codes. This extraction process removes redundant information while preserving the core semantic relationships needed for accurate retrieval, thereby reducing computational complexity without significantly compromising retrieval accuracy.
Solution Approach 2:
The patent changes the parameter representation from continuous high-dimensional floating-point vectors to discrete binary codes. This parameter transformation reduces the memory footprint and computational requirements from O(d) for high-dimensional vectors to O(b) for binary codes where b << d, while maintaining the semantic discrimination capability through optimized binary embedding algorithms.
2Reliability
If the number of candidate answers is increased, then retrieval completeness is improved, but processing time increases
Solution Approach 1:
The patent creates compressed binary code copies of semantic embeddings that can be rapidly compared. Instead of computing expensive similarity metrics on high-dimensional vectors for all candidate answers, the system uses lightweight binary code comparisons that can be processed in parallel, enabling exhaustive search over large candidate sets without linearly increasing processing time.
Solution Approach 2:
The patent performs exhaustive comparison of binary codes against all candidate answers (excessive action) rather than using approximate nearest neighbor methods. The compact binary representation makes this exhaustive search computationally feasible, ensuring complete retrieval of all relevant candidates while maintaining acceptable processing times through the efficiency of binary operations.
3Quantity of substance
If memory capacity is increased to store more candidate answers, then retrieval capacity is improved, but system cost increases
Solution Approach 1:
The patent extracts only the essential discriminative features needed for retrieval by encoding semantic information into compact binary codes. This extraction reduces the storage requirements from high-dimensional floating-point vectors to minimal binary representations, increasing the number of storable candidates in fixed memory capacity without requiring additional hardware resources.
Solution Approach 2:
The patent transforms the storage parameter from continuous high-dimensional vectors requiring significant memory bandwidth and capacity to discrete binary codes that can be stored in compact formats. This parameter change enables storing billions of candidate embeddings in consumer-grade GPU memory, dramatically increasing retrieval capacity without proportionally increasing system cost.
4Loss of information
If conventional embedding methods are used, then semantic representation is maintained, but the curse of dimensionality affects performance
Solution Approach 1:
The patent extracts the essential semantic discriminative power from high-dimensional embeddings by identifying and encoding only the most informative features into binary codes. This extraction eliminates the curse of dimensionality effects while preserving the semantic relationships needed for accurate retrieval, as the binary codes capture the essential separation between different semantic concepts.
Solution Approach 2:
The patent changes the representation parameter from high-dimensional continuous space to low-dimensional binary space, fundamentally altering the geometric properties of the embedding space. This parameter transformation eliminates the curse of dimensionality by reducing the ambient dimension while maintaining the relative distances and similarities through optimized binary encoding schemes that preserve semantic structure.
Data Source
AI summary
A computational search method for retrieving computer information related to a query includes transforming a plurality of candidate answers to candidate answer recurrent binary embedding (RBE) embeddings using a trained RBE model. A query is transformed to a query RBE embedding using the trained RBE model. The query RBE embedding is compared to each candidate answer RBE embedding of a plurality of candidate answer RBE embeddings using a similarity function. The candidate answers are sorted based on the comparisons made using the similarity function, and returning a plurality of the top candidate answers.