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katadh t1_j7s73hw wrote

Reply to comment by currentscurrents in [Discussion] Cognitive science inspired AI research by theanswerisnt42

SNN - ANN conversion and surrogate gradient methods can both get good results these days, so training has become a lot more comparable to ANNs than it was in the past. I would agree though that there is a disconnect between the hardware and software still which is preventing SNNs from reaching the dream of super low power models.

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currentscurrents t1_j7sri62 wrote

SNN-ANN conversion is kludge - not only do you have to train an ANN first, it means your SNN is incapable of learning anything new.

Surrogate gradients are better! But they're still non-local and require backwards passes, which means you're missing out on the massive parallelization you could achieve with local learning rules on the right hardware.

Local learning is the dream, and would have benefits for ANNs too: you could train a single giant model distributed across an entire datacenter or even multiple datacenters over the internet. Quadrillion-parameter models would be technically feasible - I don't know what happens at that scale, but I'd sure love to find out.

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