Multi-Branch Activation Fusion for Deep Network Accuracy
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Solution Overview
Problem
As the number of layers in neural networks increases, the speed of processing becomes slower due to poor parallelism and the need for nonlinear activation functions, leading to reduced network accuracy, especially in deep networks.
Innovation Solution
Implementing an activation unit with multiple processing branches, each corresponding to a different activation function, and fusing their results to enhance nonlinearity and improve network accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the number of layers in neural networks increases to improve network accuracy, then network accuracy is improved, but processing speed becomes slower
Solution Approach 1:
The activation function is segmented into multiple processing branches, each handling different computational paths. This allows parallel processing of operations that would traditionally be sequential, thereby improving processing speed while maintaining the depth needed for high accuracy.
Solution Approach 2:
The patent introduces a new dimensional structure by organizing activation functions into multiple branches with different computation paths. This dimensional reorganization enables simultaneous execution of multiple operations, resolving the speed-accuracy tradeoff by adding computational parallelism.
2Manufacturing precision
If nonlinear activation functions are added after linear layers to improve network accuracy, then network accuracy is improved, but processing speed becomes slower due to poor parallelism
Solution Approach 1:
The nonlinear activation function is segmented into multiple processing branches, allowing different parts of the computation to be executed in parallel. This segmentation maintains the nonlinear transformation capability needed for accuracy while enabling parallel execution to improve speed.
Solution Approach 2:
Multiple processing branches are merged into a unified activation unit that produces a single output. This merging allows parallel computation paths to be combined efficiently, achieving both the nonlinear transformation required for accuracy and the parallel execution needed for speed.
3Manufacturing precision
If multiple processing branches with different activation functions are used to increase nonlinearity and improve network accuracy, then network accuracy is improved, but device complexity increases
Solution Approach 1:
The activation unit is designed as a universal module that can handle multiple processing branches with different activation functions. This multi-functional design allows the same structural pattern to be reused across different branches, managing complexity through standardization rather than increasing it through customization.
Solution Approach 2:
The activation unit dynamically selects and executes appropriate processing branches based on input characteristics. This dynamic behavior allows the system to adapt to different computational needs without requiring a fixed complex structure, managing complexity through flexibility rather than rigid design.
Data Source
AI summary
This application discloses a data processing method relating to the field of artificial intelligence, and is for an activation unit in a neural network. The activation unit includes a plurality of processing branches. The method includes: performing activation processing on input data via each processing branch of the plurality of processing branches based on a corresponding activation function, to obtain a plurality of processing results; and fusing the plurality of processing results, to obtain a target processing result. In this application, a nonlinearity enhancement activation function is obtained by fusing a plurality of activation functions, to increase nonlinearity of the activation function, and further improve network accuracy.