Neural Network Pruning via Padding Layers for Accuracy
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Solution Overview
Problem
Existing machine learning models, particularly neural networks, face challenges in reducing calculation times and memory usage while maintaining inference accuracy, as excessive pruning degrades accuracy and current methods are inefficient in selecting layers for pruning across different types of layers in neural networks.
Innovation Solution
The solution involves inserting padding layers in attention mechanisms of neural networks to ensure tensors QT and KT have the same number of elements, allowing for efficient pruning across layers regardless of their type, using techniques like L1 regularization to determine pruning rates that maintain accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pruning is applied to reduce model size and calculation duration, then productivity is improved, but manufacturing precision deteriorates due to degradation of inference accuracy
Solution Approach 1:
The patent applies different pruning rates to different layers of the neural network based on their importance and characteristics. Critical layers maintain higher precision with lower pruning rates, while less critical layers can tolerate higher pruning rates. This local differentiation allows the system to reduce overall model size and calculation duration while preserving inference accuracy in critical pathways.
Solution Approach 2:
The patent performs preliminary analysis to identify and protect critical layers before applying pruning. By pre-identifying layers that are essential for maintaining inference accuracy, the system can apply pruning strategies that prioritize these layers, ensuring that accuracy degradation is minimized while still achieving model compression and faster calculation.
2Quantity of substance
If excessive pruning is applied to reduce model size, then productivity is improved, but manufacturing precision deteriorates significantly
Solution Approach 1:
The patent implements dynamic pruning rate adjustment where the pruning rate for each layer is not fixed but adapted based on the layer's contribution to inference accuracy and its position in the network. This dynamic approach allows the system to achieve significant model size reduction while maintaining accuracy by being more aggressive in pruning less critical parameters and more conservative in critical ones.
Solution Approach 2:
The patent changes the pruning rate parameter across different layers rather than applying a uniform pruning rate. By adjusting this parameter locally based on layer characteristics, the system can reduce model size effectively while preventing excessive pruning in layers where it would cause significant accuracy degradation.
3Ease of operation
If uniform pruning is applied across all layers, then ease of operation is improved, but manufacturing precision deteriorates due to lack of layer-specific optimization
Solution Approach 1:
The patent implements layer-specific pruning rates that are determined automatically based on each layer's characteristics and importance. This approach maintains ease of operation by providing an automated method for determining appropriate pruning rates for each layer, eliminating the need for manual tuning while preserving inference accuracy through localized optimization.
Solution Approach 2:
The system performs self-analysis to automatically determine optimal pruning rates for each layer based on their contribution to inference accuracy. This self-service capability allows the system to achieve layer-specific optimization without requiring manual intervention, maintaining ease of operation while improving manufacturing precision through adaptive, layer-aware pruning strategies.
Data Source
AI summary
A method including: inserting padding layers into a downstream side of each of a Q layer and a K layer, the padding layer padding one or more elements of a tensor, the Q and K layers respectively outputting a Query and a Key, the Query and the Key being a result of an arithmetic operating process on an input tensor in an attention mechanism in the trained machine learning model of a neural network having the attention mechanism, and padding a tensor QT and a tensor KT with the padding layers associated one with each of a reduced Q layer and a reduced K layer such that the tensor QT and the tensor KT have a same number of elements, the tensor QT and the tensor KT being respectively included in the reduced Q layer and the reduced K layer in which one or more elements are reduced.