Neural Network Model Compression via Binary Gating
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Solution Overview
Problem
Deep learning models, such as BERT and GPT, require large computational resources for training and inference, making them computationally expensive and costly, especially when applied to tasks beyond their original scope, necessitating a method to compress these models and reduce computing resources.
Innovation Solution
Introducing a weighting parameter for each path or candidate operation in the neural network model, followed by regularization using L1 or L2 techniques, which identifies and removes less important features, thereby reducing the model's complexity and computational requirements while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning models are used for tasks beyond their original scope, then model versatility is improved, but computational cost increases
Solution Approach 1:
The patent segments the deep learning model by introducing binary gating mechanisms that selectively activate or deactivate specific computation paths. This allows the model to be divided into active and inactive components, enabling versatile task handling while reducing computational cost by only executing necessary segments.
Solution Approach 2:
The patent implements dynamic computation paths through learnable binary gates that adaptively control which operations are executed based on input characteristics. This dynamic approach allows the model to adjust its computational footprint in real-time, maintaining versatility while optimizing resource usage for different tasks.
2Adaptability or versatility
If model complexity is increased to handle diverse tasks, then adaptability is improved, but device complexity increases
Solution Approach 1:
The model architecture is segmented into multiple candidate operations with binary gating controls, allowing the system to select only necessary components for each task. This segmentation reduces effective model complexity while preserving adaptability through selective activation of relevant segments.
Solution Approach 2:
The patent creates a universal model framework where a single model structure can handle multiple tasks through dynamic path selection. The binary gates enable one model to perform multiple functions by activating different computation paths, reducing the need for separate specialized models and thereby reducing overall device complexity.
3Device complexity
If regularization is applied to reduce model complexity, then model complexity is reduced, but training accuracy may deteriorate
Solution Approach 1:
The patent modifies the training process by introducing binary gating parameters that are learned alongside model weights. This parameter change enables the model to learn which computations are necessary for accurate predictions, reducing complexity through selective deactivation rather than through traditional regularization that uniformly constrains all parameters.
Solution Approach 2:
The binary gating mechanism provides feedback during training about which computation paths are most useful for accurate predictions. This feedback loop allows the model to automatically adjust its complexity by reinforcing important paths and pruning less important ones, maintaining accuracy while reducing overall model complexity.
Data Source
AI summary
Domain specific model compression by providing a weighting parameter for a candidate operation of a neural network, applying the weighting parameter to an output vector of the candidate operation, performing a regularization of the weighting parameter output vector combination, compressing the neural network model according to the results of the regularization, and providing the neural network model after compression.