Robot Control Model Pruning Using Layer-Specific Channel Similarity
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Solution Overview
Problem
Existing deep learning models face challenges in efficient pruning due to lack of a standardized approach for determining the amount of channels to prune by layer, often requiring additional resources and time, which can be costly.
Innovation Solution
A method and apparatus for controlling a robot using a pruned deep learning model that determines a first pruning rate based on inference time and similarity between channels, applying layer-specific pruning rates to maintain performance and reduce model size and computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pruning techniques are applied to reduce model parameters and enable real-time operation, then model size and computation are reduced, but additional resources and time are required to determine layer-specific pruning rates
Solution Approach 1:
The system performs self-pruning by automatically determining layer-specific pruning rates based on its own channel similarity metrics, without requiring external tools, additional training data, or separate optimization processes. The pruning rates are calculated internally using the similarity information already present in the model channels.
Solution Approach 2:
The invention changes the parameter representation from uniform pruning rates applied to all layers to layer-specific pruning rates derived from channel similarity metrics. This parameter transformation enables differentiated pruning across layers, optimizing the balance between model compression and performance retention.
2Ease of manufacture
If uniform pruning rate is applied to all layers, then implementation is simple, but model performance degrades due to lack of layer-specific optimization
Solution Approach 1:
The invention applies different pruning rates to different layers based on their specific channel similarity characteristics. Each layer is pruned according to its local properties rather than a global uniform rate, allowing optimization tailored to each layer's contribution to model performance.
Solution Approach 2:
The model is segmented into multiple layers, each evaluated independently for channel similarity. This segmentation allows the pruning process to treat each layer separately, determining appropriate pruning rates for each segment based on its specific characteristics rather than applying a blanket approach.
3Productivity
If layer-specific pruning rates are determined through additional learning, then pruning effectiveness improves, but computation time and memory allocation increase
Solution Approach 1:
The channel similarity information is computed in advance during model initialization or pre-processing, before the actual pruning operation. This preliminary computation of similarity metrics enables rapid determination of pruning rates without requiring time-consuming iterative learning or optimization during the pruning phase.
Solution Approach 2:
Channel similarity metrics serve as an intermediary that bridges the gap between model structure and pruning decisions. Instead of directly optimizing pruning rates through additional learning, the system uses similarity information as a mediator to automatically determine appropriate pruning rates for each layer.
Data Source
AI summary
An apparatus for controlling a robot comprises a processor and memory storing instructions, which, when executed by the processor, cause the apparatus to determine weighting values for multiple layers in a pre-trained deep learning model based on similarities among channels within each layer. Each layer is assigned a respective weighting value. The apparatus further determines a first pruning rate for all layers based on the inference time of the model, reflecting the time required for input processing and output prediction. A second pruning rate for each layer is determined by multiplying the first pruning rate by the corresponding weighting value, followed by random channel removal at this rate to prune the layers. The apparatus then outputs a signal based on the pruned layers and uses this signal to control the robot.