Decapping and testing this wont be difficult, meaning you understand how individual transistors are located on the die to reorder raw data into planar image. I wonder if this information can be recovered using some known patterns shown to such sensor then retrieving appropriate data and analyzing.
Another interesting thing is that for training neural networks for image recognition purpose information about location of individual pixels is not necessary at all.
Another interesting thing is that for training neural networks for image recognition purpose information about location of individual pixels is not necessary at all.
Convolutions use the spatial information. I'm less sure whether attention-based approaches typically use it.
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Decapping and testing this wont be difficult, meaning you understand how individual transistors are located on the die to reorder raw data into planar image. I wonder if this information can be recovered using some known patterns shown to such sensor then retrieving appropriate data and analyzing.
Another interesting thing is that for training neural networks for image recognition purpose information about location of individual pixels is not necessary at all.
Convolutions use the spatial information. I'm less sure whether attention-based approaches typically use it.
Just move it about for a bit with frames sampled in sequence and you should be able to build up a pretty good map of the arrays of cells.