>But in principle there's nothing standing in the way of building a 100B parameter SNN.

That's awesome. In that case, I'd pivot my research if I were you. These constrained optimization problems on limited hardware are fun and I'm sure they have some legitimate uses, but LLMs have proven that scale is king. Going in the opposite direction and trying to get SNNs to scale to billion of parameters might be world changing.

Because NNs are only going to get bigger and more costly to train. If SNNs and their accelerators can speed up training and ultimately reduce costs, that would be massive. You could be the first person in the world to create a billion parameter SNN. Once you show the world that it's possible, the flood gates will open.