Neural Network Delta Feature Map Processing for Speed and Power
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Solution Overview
Problem
Current neural network technologies face challenges in processing speed and power consumption when analyzing successively input image frames in real-time, requiring efficient methods to perform neural network operations.
Innovation Solution
A method involving a processing circuit that generates a second delta feature map by performing a linear operation on a first delta feature map, loads feature values from memory for a partial region to be updated in the feature map, and updates the feature map by storing the current feature map, thereby reducing the number of operations and memory transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a neural network device processes successively input image frames using conventional methods, then it can perform real-time analysis, but it consumes excessive power and operates at low speed
Solution Approach 1:
The patent pre-calculates and stores delta feature maps representing differences between consecutive image frames before full processing. When a new frame arrives, only the delta changes need to be processed and applied to previous results, rather than reprocessing the entire frame from scratch. This preliminary preparation of difference data significantly reduces the computational workload for real-time processing.
Solution Approach 2:
The patent extracts and processes only the delta feature maps (changes between frames) rather than processing complete image frames. By separating and processing only the changed portions, the system reduces the volume of data requiring intensive neural network operations, thereby lowering power consumption while maintaining processing speed.
2Productivity
If the neural network device processes all feature maps completely, then it ensures comprehensive analysis, but it increases the number of memory transactions and reduces processing efficiency
Solution Approach 1:
The patent extracts and processes only the delta feature maps (changes between frames) rather than processing complete image frames. By separating and processing only the changed portions, the system reduces the volume of data requiring intensive neural network operations, thereby lowering power consumption while maintaining processing speed.
Solution Approach 2:
Instead of processing all feature maps completely, the patent applies partial processing by updating only the portions of feature maps that have changed between frames. This selective updating approach reduces memory transactions and computational overhead while still capturing all relevant information for accurate analysis.
Data Source
AI summary
A method of operating a neural network device configured to perform a neural network operation on successively input image frames includes generating, by a processing circuit, a second delta feature map by performing a linear operation on a first delta feature map generated based on a difference between a current image frame and a previous image frame; loading feature values as a second previous feature map onto the processing circuit from at least one memory, the loaded feature values being feature values corresponding to a first partial region to be updated in a first feature map stored in the at least one memory; generating, at the processing circuit, a second current feature map based on the second delta feature map and the second previous feature map; and updating the first feature map by storing the second current feature map in the at least one memory.