Recurrent Neural Network Conditional Processing Skipping
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Solution Overview
Problem
Conventional recurrent neural networks face limitations in flexibility due to their cyclic nature, where parts of processing cannot be skipped, leading to inefficiencies in handling invalid or noisy data such as noise in audio or black screen images in image recognition.
Innovation Solution
The proposed method introduces a data processing approach for recurrent neural networks that includes a first start node and a first end node within each recurrent unit. This allows for conditional processing where if a target processing object does not satisfy a preset condition, no processing is performed by the data processing nodes between the start and end nodes, effectively skipping redundant operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional recurrent neural networks perform cyclic processing on all input data, then complete processing is ensured, but redundant calculations occur on invalid or noisy data reducing efficiency
Solution Approach 1:
The patent introduces a conditional processing mechanism where the network can skip processing of certain recurrent units based on whether the input data satisfies preset conditions. When data is identified as invalid or noisy (does not satisfy conditions), the corresponding recurrent units are skipped, avoiding redundant calculations while maintaining complete processing for valid data.
2Adaptability or versatility
If recurrent neural networks process all sequence data uniformly, then consistency is maintained, but flexibility is reduced due to inability to skip processing steps
Solution Approach 1:
The patent transforms the static recurrent neural network structure into a dynamic one by introducing conditional processing. The network can adapt its processing path based on input data characteristics, dynamically deciding whether to execute or skip recurrent units. This is achieved through condition judgment modules that evaluate preset conditions and control the execution flow, allowing the network to be more adaptable to different data types and quality levels.
Data Source
AI summary
A data processing method and apparatus based on a recurrent neural network and a device are provided. The recurrent neural network includes multiple recurrent units, each recurrent unit includes multiple data processing nodes and a start node, at least one recurrent unit includes an end node, and at least one data processing node is included between the start node and the end node. During the processing of the first target processing object in a first recurrent unit, in a case that the first target processing object does not satisfy the first preset condition, the start node in the first recurrent unit is run to add a tag to the data processing nodes subsequent to the start node and stop addition of the tag in response to reaching the end node, and no processing is performed by the data processing nodes with the tag.