Merging Batch Normalization into Convolutional Layers
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Solution Overview
Problem
Machine-learning models often require significant computational resources and time for inference due to their complex structures, which can exceed processing budgets and hardware capabilities, especially when equipped with computationally expensive components like batch normalization and convolutional layers.
Innovation Solution
The technique involves merging batch normalization layers into convolutional layers to form a single layer that performs both functions, reducing the number of computations required for inference while maintaining accuracy and recall rates, thereby decreasing computational costs and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If batch normalization layers and convolutional layers are used in an ML model, then model accuracy and training stability are improved, but computational cost and inference time increase
Solution Approach 1:
The patent merges the batch normalization layer and convolutional layer into a single integrated layer. The batch normalization parameters (mean, variance, scaling factor, shifting factor) are combined with the convolutional layer parameters (filter weights, biases) to create a unified layer that performs both normalization and convolution in one computational pass, eliminating the need for separate layer executions while maintaining the functional benefits of both operations
Solution Approach 2:
The merged layer serves multiple functions simultaneously: it performs batch normalization (computing mean and variance, applying scaling and shifting) and convolutional filtering in a single structural unit. This multi-functional design allows the model to maintain training stability and accuracy benefits of batch normalization while achieving the feature extraction capabilities of convolutional layers without the overhead of separate layer computations
2Reliability
If batch normalization layers and convolutional layers are used in an ML model, then model accuracy and training stability are improved, but processing resources and computational budget are exceeded
Solution Approach 1:
The patent merges the batch normalization layer and convolutional layer into a single integrated layer. The batch normalization parameters (mean, variance, scaling factor, shifting factor) are combined with the convolutional layer parameters (filter weights, biases) to create a unified layer that performs both normalization and convolution in one computational pass, eliminating the need for separate layer executions while maintaining the functional benefits of both operations
Solution Approach 2:
The merged layer serves multiple functions simultaneously: it performs batch normalization (computing mean and variance, applying scaling and shifting) and convolutional filtering in a single structural unit. This multi-functional design allows the model to maintain training stability and accuracy benefits of batch normalization while achieving the feature extraction capabilities of convolutional layers without the overhead of separate layer computations
3Reliability
If multiple separate layers are used in an ML model, then model functionality and accuracy are improved, but device complexity and hardware requirements increase
Solution Approach 1:
The patent merges the batch normalization layer and convolutional layer into a single integrated layer. The batch normalization parameters (mean, variance, scaling factor, shifting factor) are combined with the convolutional layer parameters (filter weights, biases) to create a unified layer that performs both normalization and convolution in one computational pass, eliminating the need for separate layer executions while maintaining the functional benefits of both operations
Solution Approach 2:
The merged layer serves multiple functions simultaneously: it performs batch normalization (computing mean and variance, applying scaling and shifting) and convolutional filtering in a single structural unit. This multi-functional design allows the model to maintain training stability and accuracy benefits of batch normalization while achieving the feature extraction capabilities of convolutional layers without the overhead of separate layer computations
Data Source
AI summary
Techniques for compacting an ML model by replacing a linear transformation layer and a convolutional layer with a modified convolution layer. Determining the modified convolutional layer may include determining a modified bias and/or a modified filter. In some examples, before merging the layers, an output of the linear transformation layer may be provided as input to the convolution layer (e.g., the linear transformation layer may precede the convolutional layer). The linear transformation lay may include, for example, a batch normalization layer, a pooling layer, and/or the like.