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Comment by ilaksh | Hacker News Reader

How far are we from memory-based computing going from research into competitive products? I get the impression that we are already well passed the point where it makes sense to invest very aggressively to scale up experiments with things like memristors. Because they are talking about how many new nuclear reactors they are going to need just for the AI datacenters.

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The cognitive mismatch between Von Neumann's folly and other compute architectures is vast. He slowed down the ENIAC by 66% when he got ahold of it.

We're in the timeline that took the wrong path. The other world has isolinear memory, which can be used for compute, or as memory, down to the LUT level. Everything runs at a consistent speed, and hardware faults LUTs can be routed around easily.

The problem is that the competition (our current von neumann architecture) has billions of dollars of R&D per year invested.

Better architectures without the yearly investment train will no longer be better quite quickly.

You would need to be 100x to 1000x better in order to pull the investment train onto your tracks.

Don’t has been impossible for decades.

Even so, I think we will see such a change in my lifetime.

AI could be that use case that has a strong enough demand pull to make it happen.

We will see.

I think it's just ignorance and timidity on the part of investors. Memristor or memory-computing startups are surely the next trend in investing within a few years.

I don't think it's necessarily demand or any particular calculation that makes things happen. I think people including investors are just herd animals. They aren't enthusiastic until they see the herd moving and then they want in.

I don't think it's ignorant to not invest in something that has a decade long path towards even having a market, much less a large market.

I have seen at least one experiment running a language model or other neural network on (small scale) memory-based computing substrates. That suggests less than 1-2 years to apply them immediately to existing tasks once they are scaled up in terms of compute capacity.

I would have assumed it would take many years longer than that to scale something like this up, based on how long it takes traditional CPU manufacturers to design state of the art chips and manufacturing processes.

Many more years than that. And it must be general enough. Otherwise you optimize for A in hardware and 5yr later when producing chips, A is no longer revenant and everyone moved to B.

If you don't worry about the programming model, it's pretty easy to be way better than than existing methodologies in terms of pure compute.

But if you do pay attention to the programming model, they're unusable. You'll see that dozens of these approaches have come and gone, because it's impossible to write software for them.

GPGPU is instructive. It is not easy, but possible to write software for it. That's why it succeeded.

And think of the embedded applications.

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