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YC is wrong about LLMs for chip design

zach.be

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Re: YC is wrong about LLMs for chip design

#231

Post by famouswaffles »

kuhewa wrote: post #130
famouswaffles wrote: post #94

Earlier quoted context omitted.

>If I can choose between having a script that queries the db and generates a report and “Dave in marketing” who “has done it for years” If you could that would be nice wouldn't it? And if you couldn't? If people were saying, "let's replace Casio Calculators with interfaces to GPT" then that would be crazy and I would wholly agree with you but by and large, the processes people are scrambling to place LLMs in are ones…

> by and large, the processes people are scrambling to place LLMs in are ones that typical machines struggle or fail I'm pretty sure they are scrambling to put them absolutely anywhere it might save or make a buck (or convince an investor that it could)

If your task was being solved well by a deterministic script/algorithm, you are not going to save money porting to LLMs even if you use Open Source models.

Re: YC is wrong about LLMs for chip design

#232

Post by famouswaffles »

blincoln wrote: post #203
kuhewa wrote: post #130

Earlier quoted context omitted.

> by and large, the processes people are scrambling to place LLMs in are ones that typical machines struggle or fail I'm pretty sure they are scrambling to put them absolutely anywhere it might save or make a buck (or convince an investor that it could)

100%, and a lot of them are truly terrible use cases for LLMs. For example, using a LLM to transform structured data into JSON, and doing it with two LLMs in parallel to try to catch the inevitable failures, instead of just writing code that outputs JSON.

Your example does not make much sense (in response to OP). That's not saving anybody any money.

Re: YC is wrong about LLMs for chip design

#234

Post by kuhewa »

famouswaffles wrote: post #231
kuhewa wrote: post #130

Earlier quoted context omitted.

> by and large, the processes people are scrambling to place LLMs in are ones that typical machines struggle or fail I'm pretty sure they are scrambling to put them absolutely anywhere it might save or make a buck (or convince an investor that it could)

If your task was being solved well by a deterministic script/algorithm, you are not going to save money porting to LLMs even if you use Open Source models.

'could' is doing a whole lot of work in that sentence, I'm being charitable. Reality is LLMs are being crammed in places where it isn't very sensible under thin justifications, just like the last few big ideas were (c.f. blockchain)

Re: YC is wrong about LLMs for chip design

#235

Post by blitzar »

numpad0 wrote: post #186
klabb3 wrote: post #10

I don’t mind LLMs in the ideation and learning phases, which aren’t reproducible anyway. But I still find it hard to believe engineers of all people are eager to put a slow, expensive, non-deterministic black box right at the core of extremely complex systems that need to be reliable, inspectable, understandable…

I think I've come to terms with it: engineering and making money from engineering are two completely unrelated things, the latter don't even need technology(but scamming is unethical)

Zero dollars isn't cool. You know what is? Hundreds of billions of dollars. (quote rescaled for engineering wealth and LLM wealth)

Re: YC is wrong about LLMs for chip design

#236

Post by foldr »

sincerely wrote: post #216
astrange wrote: post #189

Earlier quoted context omitted.

Diffusion models aren't actually LLMs, they're a different architecture. Which makes it even weirder we invented them at the same time. Also, they might not be able to do it. eg most models can't generate "horse riding an astronaut" or "upside-down car".

To be fair, most humans can't draw any better than stick figures.

This is true, but humans are much better at including specified elements in an image with specified spatial relationships. A description like a "A porpoise seated at a desk writing a letter" will reliably produce (terrible) drawings consisting of parts corresponding to the porpoise, parts corresponding to the desk, and parts corresponding to the letter, with the arrangement of the parts roughly corresponding to the description.

Re: YC is wrong about LLMs for chip design

#237

Post by nferraz »

> While LLMs are capable of writing functional Verilog sometimes, their performance is still subhuman.

The key word here is "still".

We don't know what the limits of LLMs are.

It's possible that they will reach a dead end. But it is also possible that they will be able to do logic and math.

If (or when) they achieve that point, their performance will quickly become "superhuman" in these kinds of engineering tasks.

But the very next step will be the ability to do logic and math.

Re: YC is wrong about LLMs for chip design

#238

Post by Joker_vD »

nferraz wrote: post #237

> While LLMs are capable of writing functional Verilog sometimes, their performance is still subhuman. The key word here is "still". We don't know what the limits of LLMs are. It's possible that they will reach a dead end. But it is also possible that they will be able to do logic and math. If (or when) they achieve that point, their performance will quickly become "superhuman" in these kinds of engineering tasks. Bu…

Reportedly, they've already hit the dead end: the newest Orion is marginally better than previous ChatGPT model (it's also marginally worse than it in some applications), and there is just no more fresh, non-AI generated data of somewhat good quality to train on.

Re: YC is wrong about LLMs for chip design

#240

Post by _1tem »

Anything that requires deep “understanding” or novel invention is not a job for a statistical word regurgitator. I’ve yet to see a single example, in any field, of an LLM actually inventing something truly novel (as judged by the experts in that space). Where LLMs shine is in producing boilerplate -- though that is super useful. So far I have yet to see anything resembling an original “thought” from an LLM (and I use AI at work every day).
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