Put simply: SIUN’s efficiency beat the CNN, and it wasn’t even close. The researchers found that SUIN “achieves a 435.01x reduction in the number of FLOPS required,” to classify the bearings within the dataset. While that was the researchers’ best-case example, other tested datasets also found significant boosts to efficiency, with SIUN reducing compute requirements about 8-fold to 27-fold when compared to a CNN.
Put simply: SIUN’s efficiency beat the CNN, and it wasn’t even close. The researchers found that SUIN “achieves a 435.01x reduction in the number of FLOPS required,” to classify the bearings within the dataset. While that was the researchers’ best-case example, other tested datasets also found significant boosts to efficiency, with SIUN reducing compute requirements about 8-fold to 27-fold when compared to a CNN.