AI Data Blocks
AI Processing Data blocks pulled from ipfs via IPFS CID/UUID
Chunk processing is similar to how jpeg blocks are processed but block size can be anything 4x4, 8x8, 12x16, etc.
It also is very similar to how a GPU handles tiling.
Job Data Structure.
(Picture of Soccer Player in Picture Above)
Chunk (Macro & Micro Job) Data Structure
(Macro -> Grid in picture Block-n, Block m are Macro jobs, AC01, AC07 & Tiles in second picture are Microjobs)
Access Patterns of the Chunk/Blocks of Data
The block stride is the number of bytes to get you to the next block.
if block stride = width of block in bytes, then processing of blocks source the blocks horizontally.
if block stride = line width of data * height of block, then processing of blocks source the blocks vertically
Other access patterns can be achieved
for instance if block stride = 2 * width of data * height of block, then processing of blocks will source every 2nd block in the horizontal direction
Those parameters allow you sample in any pattern. Blocks could be top to bottom in a column, you can sample in a row or even diagonally given block stride value
The line stride is the number of bytes to get you to the next line in the block (usually the width of the data).
The block width is the number of bytes in single line of a block. Height of the block is the number of lines of the block.
Offset is starting byte position of ipfs data.
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