Neural Network Weight Matrix Compression via Spatial Locality
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Solution Overview
Problem
Training neural networks is inefficient due to large data sizes and high memory and communication bandwidth requirements, which can take days to weeks even on cutting-edge hardware, and current compression techniques do not effectively reduce the memory footprint and bandwidth needed for weight matrices.
Innovation Solution
Implementing spatial locality in weight matrices by segmenting them into sub-components, applying a generalized weight distribution, transforming, and cropping high-frequency weights to generate low-frequency components, thereby reducing the data size transmitted during neural network training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If weight matrices are compressed using traditional methods, then data size is reduced, but memory footprint and bandwidth requirements remain high
Solution Approach 1:
The weight matrix is segmented into multiple blocks, allowing independent compression of each block. This segmentation enables the application of low-rank decomposition to individual blocks, reducing overall memory footprint while maintaining the ability to process large weight matrices efficiently
Solution Approach 2:
The patent changes the parameter representation of weight matrices by decomposing them into low-rank components (U and V matrices). This parameter transformation reduces the effective data size from O(n*m) to O(k*(n+m)) where k is the rank, significantly reducing memory requirements and bandwidth needs
2Measurement precision
If full precision weight matrices are used during training, then model accuracy is maintained, but training time increases to days or weeks
Solution Approach 1:
The patent applies partial compression by retaining the most significant low-rank components while discarding less important information. This partial action approach maintains sufficient model accuracy for practical applications while dramatically reducing training time from weeks to more manageable periods
3Weight of stationary object
If compression techniques are applied to weight matrices, then memory footprint is reduced, but communication bandwidth requirements increase
Solution Approach 1:
The low-rank decomposition is performed in advance during model initialization or preprocessing stages. This preliminary action prepares compressed weight representations that can be efficiently communicated and stored, reducing both memory footprint and subsequent communication bandwidth requirements during distributed training
Data Source
AI summary
A system, having a memory that stores computer executable components, and a processor that executes the computer executable components, reduces data size in connection with training a neural network by exploiting spatial locality to weight matrices and effecting frequency transformation and compression. A receiving component receives neural network data in the form of a compressed frequency-domain weight matrix. A segmentation component segments the initial weight matrix into original sub-components, wherein respective original sub-components have spatial weights. A sampling component applies a generalized weight distribution to the respective original sub-components to generate respective normalized sub-components. A transform component applies a transform to the respective normalized sub-components. A cropping component crops high-frequency weights of the respective transformed normalized sub-components to yield a set of low-frequency normalized sub-components to generate a compressed representation of the original sub-components.