Neural Network Filter Pruning for IoT Transmission
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Solution Overview
Problem
The high computational complexity and battery consumption of neural networks in IoT devices, combined with concerns over data privacy and latency, necessitate efficient communication and representation formats for neural networks to enable effective transmission among devices.
Innovation Solution
An apparatus and method that train neural networks using an optimization loss function considering empirical errors and model redundancy, prune insignificant filters based on normalized cross-correlations and scaling factors, and provide the pruned network for transmission, with optional retraining to maintain accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If neural network is used for intelligent applications in IoT devices, then intelligent processing capability is improved, but computational complexity and battery consumption increase
Solution Approach 1:
The patent extracts and removes redundant filters from the neural network that do not contribute significantly to classification performance. By identifying and eliminating these unnecessary components through diversity analysis and scaling factor evaluation, the network size and computational load are reduced while maintaining intelligent processing capability.
Solution Approach 2:
The patent changes the parameter of filter importance by introducing diversity measurement and scaling factor analysis. Instead of treating all filters equally, the method dynamically evaluates each filter's contribution through cross-correlation analysis and adjusts the network structure based on these parameter changes, enabling efficient pruning of redundant elements.
2Reliability
If more filters are kept in neural network, then classification performance is improved, but model size and transmission time increase
Solution Approach 1:
The patent extracts only the essential filters needed for optimal performance by removing redundant ones. Through diversity analysis and scaling factor evaluation, it identifies and extracts the minimal set of filters that maintain classification accuracy, thereby reducing model size and transmission time without sacrificing reliability.
Solution Approach 2:
The patent applies partial action by pruning only the redundant portion of filters while retaining the essential ones. Rather than removing excessive filters that would harm performance, the method carefully identifies and removes only the unnecessary subset, maintaining optimal classification performance with reduced model size.
3Device complexity
If redundant filters are removed from neural network, then model compression is improved, but classification performance may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms through diversity analysis and scaling factor evaluation to monitor filter importance before pruning. By continuously assessing filter contributions and using this feedback to guide the pruning process, the method ensures that only truly redundant filters are removed, preventing performance deterioration while achieving model compression.
Solution Approach 2:
The patent performs preliminary analysis of filter diversity and scaling factors before actual pruning occurs. This preliminary action identifies which filters are safe to remove, allowing the system to compress the model while预先 ensuring that classification performance will be maintained by keeping essential filters intact.
Data Source
AI summary
There is provided an apparatus comprising means for performing: training a neural network by applying an optimization loss function, wherein the optimization loss function considers empirical errors and model redundancy (210); pruning a trained neural network by removing one or more filters that have insignificant contributions from a set of filters (220); and providing the pruned neural network for transmission (230).