If we suppose that ANNs are more or less accurate models of real neural networks, the reason why they're so inefficient is not algorithmic, but purely architectural. They're just software. We have these huge tables of numbers and we're trying to squeeze them as hard as possible through a relatively small number of multipliers and adders. Meanwhile, a brain can perform a trillion fundamental simultaneously because every neuron is a complete processing element independent of every other one. To bring that back into more concrete terms, if we took an arbitrary model and turned it into a bespoke piece of hardware, it would certainly be at least one or two orders of magnitude faster and more efficient, with the downside that since it's dead silicon it could not be changed and iterated on.

If you account for the fact that biological neurons operate at a much lower frequency than silicon processors, then the raw performance gets much closer. From what I can find, neuron membrane time constant is around 10ms [1], meaning 10 billion neurons could have 1 trillion activations per second, which is in the realm of modern hardware.

People mentioned in [2] have done the calculations from a more informed position than I have, and reach numbers like 10^17 FLOPS when doing a calculation that resembles this one.

[1] https://spectrum.ieee.org/fast-efficient-neural-networks-cop...

[2] https://aiimpacts.org/brain-performance-in-flops/

the reason why they're so inefficient is not algorithmic, but purely architectural.

I would agree with that, with the caveat that in my mind "the architecture" and "the algorithm" are sort of bound up with each other. That is, one implies the other -- to some extent.

And yes, fair point that building dedicated hardware might just be part of the solution to making something that runs much more efficiently.

The only other thing I would add, is that - relative to what I said in the post above - when I talk about "algorithmic advances" I'm looking at everything as potentially being on the table - including maybe something different from ANN's altogether.

The energy inefficiency of ANNs vs our brain is mostly because our brain operates in async dataflow mode with each neuron mostly consuming energy only when it fires. If a neuron's inputs haven't changed then it doesn't redundantly "recalculate it's output" like an ANN - it just does nothing.

You could certainly implement an async dataflow type design in software, although maybe not as power efficiently as with custom silicon, but individual ANN node throughput performance would suffer given the need to aggregate neurons needing updates into a group to be fed into one the large matrix multiplies that today's hardware is optimized for, although sparse operations are also a possibility. OTOH conceivably one could save enough FLOPs that it'd still be a win in terms of how fast an input could be processed through an entire neural net.

> If we suppose that ANNs are more or less accurate models of real neural networks

i believe the problem is we don't understand actual neurons let alone actual networks of neurons to even know if any model is accurate or not. The AI folks cleverly named their data structures "neuron" and "neural network" to make it seem like we do.

> If we suppose that ANNs are more or less accurate models of real neural networks [..]

IANNs were inspired by biological neural structures and that's it. They are not representative models at all, even of the "less" variety. Dedicated hardware will certainly help, but no insights into how much it can help will come from this sort of comparison.