Neural Network Channel Pruning for Device-Specific Latency
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Solution Overview
Problem
Convolutional neural networks (CNNs) are computationally intensive, leading to large model sizes that are difficult to deploy on devices with limited performance, such as mobile devices and edge computing devices, and existing pruning techniques provide limited improvements in computing speed and efficiency.
Innovation Solution
A method and apparatus that adjust the number of output channels in a neural network model based on device-specific latency characteristics to optimize performance and reduce model size, using techniques like structured layer-adaptive sparsity and filter decomposition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CNN models are used to improve performance, then accuracy and computational capability are improved, but model size and computational requirements increase
Solution Approach 1:
The patent extracts and removes unnecessary channels from convolutional layers through automated channel pruning. By identifying and eliminating redundant channels based on importance metrics, the model achieves compression while maintaining performance, directly addressing the contradiction between model size and performance
Solution Approach 2:
The patent applies different pruning strategies to different channels within the same layer based on their individual importance. Instead of uniform pruning, it selectively prunes only the least important channels in each layer, preserving critical features while removing redundancy, thus maintaining performance with reduced model size
2Quantity of substance
If weight pruning is used to reduce model size, then compression ratio is improved, but computing speed on general-purpose hardware deteriorates or improves only limitedly
Solution Approach 1:
The patent segments the convolutional layers into groups and applies structured pruning at the channel level rather than individual weight level. This segmentation approach maintains the structured sparsity pattern that is friendly to general-purpose hardware, enabling better computation speed while achieving significant compression
Solution Approach 2:
The patent changes the architecture parameters by adjusting the number of channels in each layer rather than modifying individual weights. This parameter-level modification maintains computational efficiency on general-purpose hardware while achieving high compression ratios through automated channel selection and removal
3Speed
If filter pruning is used to reduce model size, then computing speed is improved and hardware compatibility is enhanced, but manual channel selection is required
Solution Approach 1:
The patent implements automated channel pruning through feedback mechanisms that evaluate channel importance based on model performance metrics. The system automatically selects channels for pruning based on computed importance scores, eliminating the need for manual intervention while maintaining computing speed improvements and hardware compatibility
Solution Approach 2:
The patent enables the system to self-determine which channels to prune based on automated importance assessment. The model automatically identifies and removes redundant channels without human intervention, making the process self-service oriented while preserving the benefits of filter pruning for computing speed and hardware compatibility
4Quantity of substance
If channel pruning is applied to reduce model size, then model compression is improved, but latency characteristics must be optimized for specific hardware
Solution Approach 1:
The patent dynamically adjusts the number of channels in each layer based on hardware-specific latency characteristics. Instead of using fixed pruning ratios, the system adapts channel dimensions to match the temporal constraints and performance characteristics of target hardware platforms, achieving both compression and latency optimization
Solution Approach 2:
The patent modifies architectural parameters including channel dimensions and layer configurations to optimize latency performance. By adjusting these parameters based on hardware characteristics, the system achieves model compression while controlling inference time, balancing size reduction with temporal efficiency
Data Source
AI summary
Provided are a method and apparatus for compressing a neural network model by using device characteristics. The method includes: obtaining the neural network model that is executed by a device; adjusting a target number of output channels of a target layer included in the neural network model, based on an arithmetic intensity obtained from a roofline model and a latency characteristic of a staircase pattern of the device; and compressing the neural network model such that the number of output channels of the target layer is equal to the adjusted target number of output channels.