Neural Network Compression via Trainable Bottleneck Parameters
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Solution Overview
Problem
Existing neural network pruning methods fail to efficiently preserve model accuracy while reducing floating point operations per second (FLOPs), often requiring extensive computational resources and numerous finetuning epochs, and do not effectively handle limited computational resources.
Innovation Solution
An automatic lightweight method that injects trainable bottleneck parameters into a neural network model, trains these parameters, determines an optimal threshold for pruning, and prunes the model based on the trained parameters to maintain accuracy while minimizing FLOPs, using a dichotomy algorithm to adjust the threshold and ensure efficient information flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If pruning filters is applied to reduce model size, then the number of parameters and FLOPs is reduced, but model accuracy deteriorates
Solution Approach 1:
The method performs preliminary training of bottleneck parameters before actual pruning. By training these parameters in advance on the full model, the method identifies which filters are most important to retain, ensuring that accuracy is preserved before any filters are removed. This preliminary action allows the pruning process to be guided by learned importance scores rather than random or heuristic selection.
Solution Approach 2:
The method introduces bottleneck parameters as trainable variables that control the flow of information through different filters. By optimizing these parameters during training, the model learns to assign different weights to different filters, effectively identifying which ones are most important. This parameter change approach transforms the discrete pruning decision into a continuous optimization problem that can be solved during training.
2Reliability
If iterative pruning or retraining is performed to maintain accuracy, then model accuracy is preserved, but computational time and resources increase
Solution Approach 1:
The method performs all necessary training in the preliminary phase before pruning. By training the bottleneck parameters on the complete model with all filters present, the method captures the full interdependencies between filters. This eliminates the need for iterative retraining after pruning, as the bottleneck parameters already encode the optimal filter selection for maintaining accuracy.
Solution Approach 2:
The method separates the pruning decision-making process from the model training process. The bottleneck parameters are trained independently to determine filter importance, and once trained, these parameters directly guide the pruning operation without requiring additional training iterations. This segmentation allows the two processes to be optimized independently and executed in a single pass.
3Ease of manufacture
If manual definition of pruning percentage is performed, then pruning can be controlled, but the process becomes time-consuming and requires human expertise
Solution Approach 1:
The method enables the model to automatically determine its own pruning configuration through the learned bottleneck parameters. Instead of requiring external human expertise to define pruning percentages, the model itself learns which filters are important and which can be removed. The bottleneck parameters serve as self-generated guidance signals that automatically adapt to the specific model architecture and dataset.
Solution Approach 2:
The method implements a feedback mechanism where the bottleneck parameters are trained using the model's performance on the training data. The loss function provides feedback about which filters are contributing most to accurate predictions, and this feedback is used to update the bottleneck parameters. This automated feedback loop replaces manual human judgment with data-driven automatic decision-making.
Data Source
AI summary
Disclosed is an automatic lightweight method and apparatus for information flow-based neural network compression model that may preserve a performance. An automatic lightweight method for a neural network model may include receiving a first model, generating a second model by injecting trainable bottleneck parameters into the first model, training the bottleneck parameters of the second model using training data, determining an optimal threshold for the trained bottleneck parameters, and pruning the second model based on the trained bottleneck parameters and the determined optimal threshold.