Learned Augmented Residual Layers for Compact Neural Networks
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Solution Overview
Problem
Conventional neural networks become computationally expensive and resource-intensive when increased complexity is required for complex tasks, limiting their practical application in real-time scenarios.
Innovation Solution
Implementing a neural network with learned augmented residual layers (LAuReL) that combine initial block outputs with block inputs using learned parameters, allowing for improved performance without significantly increasing footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of layer blocks is increased to improve ML task performance, then the neural network can handle more complex tasks, but the compute resource requirement and neural network footprint increase significantly
Solution Approach 1:
The patent changes the functional parameters of residual layers by introducing learned augmentation transformations that dynamically modify residual connections. Instead of simply adding more layers, the patent modifies how existing layers process information through learned parameters that adapt residual behavior, achieving better performance without increasing network depth or width.
Solution Approach 2:
The patent applies local quality by making residual layers heterogeneous through learned augmentations. Different residual layers can have different augmentation transformations learned and applied, allowing each layer to specialize in specific feature transformations rather than applying uniform operations throughout the network.
2Reliability
If additional layer blocks are added to increase neural network complexity, then performance on complex ML tasks improves, but training time and inference latency increase prohibitively
Solution Approach 1:
The patent modifies the operational parameters of residual layers by introducing learned augmentation transformations. This allows the network to achieve complex task performance through more sophisticated parameterized transformations rather than through increased network depth, thereby reducing training time and inference latency while maintaining or improving performance.
3Reliability
If the neural network footprint is increased to improve performance, then more complex ML tasks can be performed, but practical application in real-time scenarios is limited
Solution Approach 1:
The patent achieves better performance through learned parameter transformations in residual layers rather than through increased network size. This parameter-based approach maintains computational efficiency and reduces memory requirements, making the network more suitable for real-time applications while improving performance on complex tasks.
Data Source
AI summary
Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for processing a network input using a neural network to generate a network output for the network input. That is, by using a neural network that includes a sequence of layer blocks that, for each layer block, processes a block input for the particular layer block through a learned non-linear transformation to generate an initial block output for the particular layer block and combines the initial block output for the particular layer block with at least the block input in accordance with one or more learned parameters to generate the block output for the particular layer block, the described techniques maximize the neural network performance for a given neural network footprint.