Clustering-Based Quantization for Neural Network Weight Tensor Compression
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Solution Overview
Problem
Current neural network representation (NNR) coding systems face challenges in efficiently compressing neural network models, particularly in reducing storage and transmission bandwidth, due to limitations in effectively handling the complexity and dimensionality of weight tensors in convolutional neural network (CNN) layers.
Innovation Solution
The implementation of clustering-based quantization methods, such as hierarchical or k-means clustering, which identifies and separates outliers from inliers in weight tensors, rearranges weights into lower-dimensional matrices, and applies vector quantization to preserve cross-kernel correlation, allowing for efficient compression and decompression of neural network models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional NNR coding systems are used to compress neural network models, then storage and transmission bandwidth are reduced, but the complexity and dimensionality of weight tensors in CNN layers cannot be effectively handled
Solution Approach 1:
The patent segments weight tensors into multiple clusters using k-means clustering algorithm. Each cluster represents a group of similar weight values, allowing the high-dimensional weight tensor to be decomposed into multiple lower-dimensional cluster representations. This segmentation reduces the complexity of handling the entire weight tensor while preserving the essential information distribution.
Solution Approach 2:
The patent transforms the weight tensor from its original high-dimensional space into a lower-dimensional space by projecting weights onto cluster centers. This dimensionality reduction is achieved by representing each weight as a combination of cluster center positions and membership probabilities, effectively converting the complex high-dimensional tensor into a simpler representation that maintains the essential structure.
2Quantity of substance
If clustering-based quantization is applied to reduce weight tensor dimensionality, then storage efficiency is enhanced, but quantization errors may increase
Solution Approach 1:
The patent changes the parameter representation by introducing cluster membership probabilities in addition to cluster center positions. Instead of directly quantizing weight values to fixed levels, the method represents weights as probabilistic distributions over multiple clusters. This parameter transformation allows for more flexible and accurate representation, reducing quantization errors while maintaining storage efficiency.
Solution Approach 2:
The patent creates a composite representation by combining multiple cluster center vectors with their corresponding membership probabilities. This composite structure allows the system to capture both the dominant patterns (through cluster centers) and the nuanced variations (through probability distributions), achieving a balance between compression ratio and reconstruction accuracy.
3Reliability
If weight tensors are rearranged into lower-dimensional matrices, then cross-kernel correlation is preserved, but the original tensor structure is altered
Solution Approach 1:
The patent performs preliminary rearrangement of weight tensors into lower-dimensional matrices before applying clustering-based quantization. This preliminary action reorganizes the data layout to expose and preserve cross-kernel correlations, making them more accessible to the clustering algorithm. The rearrangement is designed to group related kernels together, ensuring that their correlations are maintained in the compressed representation.
Solution Approach 2:
The patent creates a transformed copy of the weight tensor in a different dimensional arrangement. This copied representation maintains the essential correlations and relationships of the original tensor while being more suitable for compression. The copying process preserves the cross-kernel correlations by strategically reorganizing the data layout, allowing the compressed representation to capture these relationships more effectively.
Data Source
AI summary
Systems, methods, and instrumentalities are disclosed for clustering-based quantization for neural network (NN) compression. A distribution of weights in weight tensors in NN layers may be analyzed to identify cluster outliers. Cluster inliers may be coded from cluster outliers, for example, using scalar and/or vector quantization. Weight-rearrangement may rearrange weights for higher dimensional weight tensors into lower dimensional matrices. For example, weight rearrangement may flatten a convolutional kernel into a vector. Correlation between kernels may be preserved, for example, by treating a filter or kernels across a channel as a point. A tensor may be split into multiple subspaces, for example, along an input and/or an output channel. Predictive coding may be performed for a current block of weights or weight matrix based on a reshaped or previously coded block or matrix. Arrangement, inlier, outlier, and/or prediction information may be signaled to a decoder for reconstruction of a compressed NN.