Neural Network Tile Segmentation for Code Compression

The patent segments the neural network processing into distinct tile types based on size and padding characteristics. By dividing the processing space into standardized tile segments (e.g., 16x16, 8x8, 4x4 with different padding values), the system can handle diverse image regions using a有限 set of predefined processing templates, reducing the need for custom code for each possible tile configuration.

The patent creates universal processing templates that can handle multiple tile types. Each template is designed to process tiles with specific size and padding combinations, and these templates are reused across different network layers and tile positions. This multi-functionality allows the same code segments to serve multiple purposes, significantly reducing the overall software code amount while maintaining processing precision.

The patent applies local quality by assigning specific processing characteristics to different tile types based on their size and padding requirements. Each tile type (e.g., 16x16 with no padding, 8x8 with padding) has an optimized processing template tailored to its specific characteristics. This allows the system to adapt to diverse tile requirements while keeping each individual processing module simple and specialized.

The patent manages adaptability by parameterizing tile processing based on size and padding values. Instead of creating complex handling logic for each possible tile variation, the system uses parameter changes to define different tile types and selects appropriate processing templates based on these parameters. This approach maintains high adaptability while avoiding the complexity of custom processing logic for each tile configuration.