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Table 2 GPU memory/FLOPs/Params/RunTime comparison on kernel estimation layer (KEL), self-attention (SA), interlaced sparse self-attention layer (SSA) and the proposed approach

From: Sparse self-attention aggregation networks for neural sequence slice interpolation

 

Memory(GB)

FLOPs(G)

Params(M)

RunTime(ms)

KEL [9]

-

138.4

-

6160

SA [27]

256.0

6599

0.0052

-

SSA [30]

9.775

81.24

0.0104

327

AAL(Ours)

2.331

10.42

0.0156

275

  1. All the numbers are tested on a single P40 GPU with CUDA10.2 and an input feature map of 1×64×512×512 during inference stage. Lower Memory, FLOPs and RunTime indicate better performance
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