Those are amazing accomplishments but I am more interested in research developments in things like In-Memory (Analog) or other different approaches.
Companies like EnCharge, Mythic, etc.
And much more efficient devices like RRAM, MRAM, and FETs. Like FE-FETs with AlScN.
The stuff just coming out of research or still in research is more exciting in terms of the potential for truly huge efficiency and performance boosts.
Taalas is interesting also because of it's efficiency and speed. Guess it was just purchased by AMD.
I'm not sure that more efficient compute can win out, since it also has to beat out the competition on capital investment. There's an upper bound of 'just get 3 sets of GPUs and run them on solar in three different timezones' where the cost of electricity is negligible. Efficient digital architectures run about 1/5 or 1/10th the speed and require much more wafer area. Analog technologies seem unlikely to have the fidelity required for training, though inference sounds plausible. It's also not clear that analog technologies would necessarily use less power.
Is anyone working on running neural networks on CPU architectures that are not limited by synchronous clock signals? Organic neural networks are inherently asynchronous and signals propagate through different parts of the network at their own pace.
Microsofts Bitnet is really solid on CPUs (though still better to train on a GPU for speedier training) but theres a lot of room for improvement in these spaces.
Yes, for over a decade now and it's already available in the cloud. It's a spinn-off of human brain project. Search for spinnaker neuromorphic computing.
It's an interesting topic, but the article is completely AI generated, which isn't surprising considering the startup, but still, if you don't even go over the output what's the point?
Maybe it's just me, but between the extremely thin font and layout design, I find this extremely difficult to parse. Overuse and improper use of italics is confusing as well.
I just used it on 4 texts. The 1st version was the output from my first (chatGPT) prompt. The 4th was the last prompts of "reduce, make it less AI, etc" of the same original request/output.
In all cases it was meatbag. In the 1st case gave chatGPT 0.35. The remaining ones were much lower.
EDIT: I didn't ask just to generate some text. The original prompt was very specific, which is most of the time the way I use LLMs anyways. Probably it catches better the "hey just generate this email...".
It’s shorthand for “it’s unreadable, but in the way an AI would write it”. I’m not OP, but that’s the issue. If it was well written (even if AI written), there would be no problem.
As it turns out, you still need to be a good writer in order to prompt an AI to write well.
def. AI-assisted, both content and style style/nav.
Content-wise, it looks legit, but with that amount of stuff, it's nearly impossible to say for sure.
I have a feeling the hardware architecture for LLMs are completely wrong. There's no way hundreds of kilowatts is required for intelligence.. just in terms of the physics. Is there someone out there in the analog/neuromorphic computing world that could make these power-hungry monsters completely redundant?
It's more the economics of what sells that lets the power hungry monsters thrive. If you can tape out one chip design, and one gives you 1 TOPS and the other is 5 TOPS, the sales will go to the 5 TOPS because power is a downstream cost. That being said, it's only a matter of time before both chips are maximum TOPS and the only differentiator is power. My latest simulations are showing a competitive design around 70W, but I'm still confirming it and it would really be beneficial to do some data gathering tapeouts at larger nodes to feel like it's worth the risk of more expensive nodes.
Yes smaller nodes give lower power operation with faster transistor switching speeds. You can also underpower a node to intentionally cause slower transistor switching speeds.
You're completely wrong about hardware being the bottleneck.
The software architecture LLMs run on was chosen precisely because hardware can brute force it and brute force has become the method of choice ever since, because it is easier to scale.
And here you're saying "what if everyone was wrong and what we need is just more brute force?", without looking at the reason that makes brute force necessary to begin with.
This is also the problem, we are continuing with LLMs because they fit hardware well, instead of experimenting with trying to model knowledge (how do I know what I know) and metacognition (what do I know).
Current architecture can't tell the difference between fact and fiction, so to speak. (Which I think is a better way to say it rather than truth and falsehood; facts have provenance and context, not just "true" or "false".)
Companies like EnCharge, Mythic, etc.
And much more efficient devices like RRAM, MRAM, and FETs. Like FE-FETs with AlScN.
The stuff just coming out of research or still in research is more exciting in terms of the potential for truly huge efficiency and performance boosts.
Taalas is interesting also because of it's efficiency and speed. Guess it was just purchased by AMD.
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