>I meant just in terms of compute efficiency, using the same kind of algorithms we use now.

For SNNs I'm sure they can make them more efficient but that doesn't mean it'll have a better ratio score/power_cons on a task than more standard models in their most optimized versions.

>This makes sense to me; instead of emulating a neural network using math, you're building a physical model of one on silicon. Plus, SNNs are very sparse and an analog one would only use power when firing.

I understand and I can't disagree but as I said, we don't have the proof that the way we're usually doing it (with dense layers / tensors) is necessarily less efficient than artificial SNNs or biologicial NNs. "Efficient" in terms of accuracy / power consumption. And we don't have a theory that would allow a generic comparison between usual ANNs and SNNs or Biological NNs, it would require a generic metric of how "intelligent" these models can be just because of their design (we don't have that). Neurons in usual ANNs don't represent the same thing.

Also, an optimized model on a modern GPU can run resnet50 (fp16) at ~2000 fps with 450W, we can't directly compare fps with human vision but if the brain works with 20W, it's equivalent to approximately 90 fps for 20W (if you say 7W are for vision, it's 30fps). Of course we don't see at 30fps and it's hard to compare the accuracy of resnet50 with humans, but resnet50 is also very far from being the most efficient architecture and there are also more power efficient GPUs. It's hard to say for sure that current GPUs with SOTA models would be less power efficient on some tasks than the human brain.

>I feel like a lot of SNN research is motivated by understanding the brain rather than being the best possible AI.

It depends on what you call the "best possible AI". It's probably not designed to be a SOTA on usual tasks but the best way to prove that you can understand the human brain is by reproducing how it works, which would make the resulting model better than current models on a lot of tasks.