Submitted by FrereKhan t3_11zg5rr in MachineLearning
FrereKhan OP t1_jdcvobu wrote
Reply to comment by CommunismDoesntWork in [P] New toolchain to train robust spiking NNs for mixed-signal Neuromorphic chips by FrereKhan
Sort of yes; Xylo is a general-purpose SNN accelerator, but the scale is for smaller problems, in the order of 1000 neurons.
But in principle there's nothing standing in the way of building a 100B parameter SNN.
Art10001 t1_jdd0ag1 wrote
Brainchip has 1 million neurons already. Loihi and Loihi2 similar.
CommunismDoesntWork t1_jdcxx5u wrote
>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.
KerfuffleV2 t1_jdd5b3d wrote
Have you already seen this? https://github.com/ridgerchu/SpikeGPT
CommunismDoesntWork t1_jdd87tx wrote
I haven't, that's really cool though!
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