Entropy-Guided Model-Level Skipping in Compressed Incremental Learning
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Solution Overview
Problem
Existing technologies for neural network weight update communication in multimedia systems are inefficient and require complex validation schemes, leading to unnecessary computational overhead and resource utilization.
Innovation Solution
Implement model level update skipping in compressed incremental learning by determining weight updates based on entropy or KL-divergence independent of tensor content, using a common reference model to decide on the necessity of weight updates, and signaling with a one-bit indication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If weight updates are communicated in every epoch, then training accuracy is maintained, but communication overhead and resource utilization increase
Solution Approach 1:
The system changes the parameter of communication frequency from every epoch to selective epochs based on entropy thresholds. By monitoring entropy values and only communicating when the threshold is exceeded, the system maintains training accuracy while reducing communication overhead and energy consumption.
Solution Approach 2:
The system implements feedback through entropy monitoring and KL-divergence calculation to determine when weight updates should be communicated. This feedback mechanism allows the system to adaptively control communication based on actual model changes, preventing unnecessary transmissions while ensuring accuracy is maintained.
2Measurement precision
If complex validation schemes are used to determine weight updates, then update accuracy is improved, but computational complexity increases
Solution Approach 1:
The system extracts the essential information needed for validation into a single entropy value and KL-divergence metric. By taking out only the necessary computational elements and using them as decision criteria, the system achieves accurate update determination without requiring complex validation schemes.
Solution Approach 2:
The validation process is segmented into independent steps: calculating entropy for each epoch, comparing against thresholds, and only performing full validation when necessary. This segmentation reduces computational complexity by performing simple checks before complex validation operations.
3Loss of energy
If weight updates are skipped without proper indication, then communication is reduced, but receiver cannot determine if update was transmitted
Solution Approach 1:
The system introduces an intermediary signaling mechanism (one-bit indication flag) that mediates between the sender's decision to skip communication and the receiver's need for information. This flag acts as a mediator that conveys update presence information without requiring actual weight data transmission, reducing communication while maintaining information flow.
Data Source
AI summary
An apparatus including: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to: determine a first value of a first epoch of training a neural network based on a relation applied to at least one weight of the neural network from the first epoch and a base model; determine a second value of a second epoch of training the neural network based on the relation applied to the at least one weight of the neural network from the second epoch and the base model; wherein the second epoch occurs later than the first epoch; and determine whether to communicate a weight update to the at least one weight of the neural network between the second epoch of training and the first epoch of training, based at least on the first value and the second value.