Resonator Network Classification Using Codebook Factorization
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Solution Overview
Problem
Neural networks require a large number of parameters for classification tasks, especially when dealing with a high number of classes, leading to increased resource consumption and computational complexity.
Innovation Solution
Implement a resonator network with a classification unit that uses a fixed classification matrix formed by codebooks, replacing fully-connected layers to perform high-dimensional vector factorization, allowing for parallel computation and reduced parameter usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a learned affine transformation is placed at the end of the neural network for classification, then classification accuracy is maintained, but the number of parameters grows linearly with the number of classes, requiring increasingly more resources for storage and computations
Solution Approach 1:
The classification matrix is segmented into multiple codebooks, where each codebook contains a subset of the total classes. Instead of using a single large affine transformation matrix with C columns (where C is the number of classes), the system divides it into multiple smaller codebooks, each handling a portion of the classification task. This segmentation reduces the memory footprint and computational complexity while maintaining the ability to classify across all C classes through coordinated use of multiple codebooks.
2Adaptability or versatility
If the number of classes in the classification task is increased, then the model's versatility is improved, but the resources required for storage and computations increase linearly
Solution Approach 1:
The system transitions from a single-dimension approach (one large classification matrix) to a multi-dimensional approach (multiple codebooks arranged in a hierarchical structure). The codebooks are organized in layers, with each layer containing multiple codebooks that handle different subsets of classes. This dimensional restructuring allows the system to scale to handle more classes without linearly increasing resource requirements, as the computational complexity grows more slowly due to the distributed nature of the codebook structure.
Data Source
AI summary
A computer-implemented method for performing a classification of an input signal utilizing a neural network includes: computing, by a feature extraction unit of the neural network, a query vector; and performing, by a classification unit, a factorization of the query vector to a plurality of codebook vectors of a plurality of codebooks to determine a corresponding class of a number of classes. A set of combinations of vector products of the plurality of codebook vectors of the plurality of codebooks establishes a number of classes of the classification unit.