That’s a valid concern from a theoretical point of view.
Note however, that a lot of operations are not compute bound, but often memory bandwidth bound.
I.e. the GPU computation would be “fast enough”, but the overall speed is limited by the reads and writes of the data.
Even if the MUL and ADD are more expensive on GPUs, which could theoretically mean that there wouldn’t be any expected speedup using Winograd, in reality the kernel performance might still be better than other (less compute-intensive) kernels.
cudnn in particular uses the benchmark mode to profile different algorithms and should only select the Winograd kernel, if it’s the fastest one (and also meets other requirements specified by the user such as determinism).