Weight Compression for Distributed Neural Network Training
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Solution Overview
Problem
Training distributed neural networks is bandwidth-intensive, leading to slower training times due to the high volume of weight data shared among nodes.
Innovation Solution
Implementing a weight manager that compresses and calculates average weight values across neural network nodes, reducing bandwidth usage by sharing these averages instead of individual weight values, allowing nodes to converge to similar configurations for parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual weight values are shared among neural network nodes, then training accuracy is maintained, but bandwidth consumption increases and training time increases
Solution Approach 1:
The patent extracts only the essential information from individual weight values by computing average weight values across nodes. Instead of transmitting full weight matrices, each node sends only the average values computed from its local weight updates, significantly reducing the data volume transmitted over the network while preserving the essential training information.
Solution Approach 2:
The patent uses average weight values as simplified copies of the full weight information. These compressed representations capture the essential updates from all nodes without requiring transmission of the complete weight matrices, enabling efficient synchronization while maintaining training effectiveness.
2Reliability
If individual weight values are shared among neural network nodes, then training accuracy is maintained, but training time increases
Solution Approach 1:
The patent extracts only the essential information from individual weight values by computing average weight values across nodes. Instead of transmitting full weight matrices, each node sends only the average values computed from its local weight updates, significantly reducing the data volume transmitted over the network and thereby reducing synchronization time.
3Loss of energy
If compressed weight data is shared among nodes, then bandwidth consumption is reduced, but weight synchronization complexity increases
Solution Approach 1:
The patent introduces average weight values as an intermediary representation between individual node weights and the distributed system state. This intermediary form simplifies the synchronization process by providing a single aggregated value that represents the collective weight updates, making the coordination mechanism more manageable despite the distributed nature of the system.
4Measurement precision
If full weight matrices are transmitted between nodes, then weight synchronization accuracy is maintained, but communication overhead increases
Solution Approach 1:
The patent extracts only the essential information from individual weight values by computing average weight values across nodes. Instead of transmitting full weight matrices, each node sends only the average values computed from its local weight updates, significantly reducing the data volume transmitted over the network while preserving the essential training information.
Data Source
AI summary
Embodiments of the present disclosure include systems and methods for compressing weights for distributed neural networks. In some embodiments, a first network comprising a first set of weights is trained using a set of training data. A second network comprising a second set of weights is trained using the set of training data. A number of weights in the first set of weights is greater than a number of weights in the second set of weights. The first set of weights are adjusted based on a first loss determined by the first network and a second loss determined by the second network. The second set of weights are adjusted based on the first loss determined by the first network and the second loss determined by the second network. Values of the second set of weights are sent to a computing system.