Neural Network Pruning for Element-Wise Layer Shape Matching
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Solution Overview
Problem
Neural networks are often overparametrized, leading to computationally complex and memory-intensive networks with redundant connections, and pruning these networks can degrade performance or accuracy, especially when input layers are pruned into element-wise operations.
Innovation Solution
A pruning engine is used to identify and deactivate neurons in neural networks based on weight metrics, applying equalization operators to ensure accurate performance of element-wise operations by maintaining the shape and size of input layers, and retraining the pruned networks to regain accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pruning is applied to neural networks to reduce complexity, then computational complexity and memory requirements are reduced, but performance and accuracy of the neural network degrade
Solution Approach 1:
The patent applies different pruning strategies to different parts of the neural network based on their specific characteristics. Element-wise operation layers are treated differently from standard convolutional layers, with selective pruning that preserves critical connections while removing redundant ones. This localized approach maintains performance in sensitive regions while reducing complexity overall.
Solution Approach 2:
The patent performs preliminary identification and classification of different layer types before applying pruning. By pre-analyzing the network structure to identify element-wise operation layers versus standard convolutional layers, the system can apply appropriate pruning strategies in advance, preventing performance degradation before it occurs.
2Device complexity
If input layers are pruned into element-wise operations, then network complexity is reduced, but execution of element-wise operations may be prevented or performance impacted
Solution Approach 1:
The patent applies selective pruning to element-wise operation layers, preserving critical connections while removing redundant ones. By analyzing the specific characteristics of each element-wise operation layer, the system maintains the execution capability of these operations while reducing overall network complexity.
Solution Approach 2:
The patent implements dynamic pruning strategies that adapt to the specific structure and requirements of element-wise operation layers. The pruning process dynamically adjusts which connections to preserve based on the operational characteristics of each layer, ensuring that element-wise operations remain functional while reducing complexity.
Data Source
AI summary
Input layers of an element-wise operation in a neural network can be pruned such that the shape (e.g., the height, the width, and the depth) of the pruned layers matches. A pruning engine identifies all of the input layers into the element-wise operation. For each set of corresponding neurons in the input layers, the pruning engine equalizes the metrics associated with the neurons to generate an equalized metric associated with the set. The pruning engine prunes the input layers based on the equalized metrics generated for each unique set of corresponding neurons.