[HN Gopher] YC is wrong about LLMs for chip design
___________________________________________________________________
YC is wrong about LLMs for chip design
Author : laserduck
Score : 175 points
Date : 2024-11-16 14:07 UTC (8 hours ago)
(HTM) web link (www.zach.be)
(TXT) w3m dump (www.zach.be)
| 0xmarcin wrote:
| This is not my domain so my knowledge is limited, but I wonder if
| the chip designers have some sort of a standard library of ready
| to use components. Do you have to design e.g. ALU every time you
| design a new CPU or is there some standard component to use? I
| think having a proven components that can be glued on a higher
| level may be the key to productivity here.
|
| Returning to LLMs. I think the problem here may be that there is
| simply not enough learning material for LLM. Verilog comparing to
| C is a niche with little documentation and even less open source
| code. If open hw were more popular I think LLMs could learn to
| write better Verilog code. Maybe the key is to persuade hardware
| companies to share their closed source code to teach LLM for the
| industry benefit?
| Filligree wrote:
| Or learning through self-play. Chip design sounds like an area
| where (this would be _hard_!) a sufficiently powerful simulator
| and /or FPGA could allow reinforcement learning to work.
|
| Current LLMs can't do it, but the assumption that that's what
| YC meant seems wildly premature.
| AlotOfReading wrote:
| There are component libraries, though they're usually much
| lower level than an ALU. For example Synopsys Designware:
|
| https://www.synopsys.com/dw/buildingblock.php
| MobiusHorizons wrote:
| The most common thing you see shared is something called IP
| which does mean intellectual property, but in this context you
| can think of it like buying ICs that you integrate into your
| design (ie you wire them up). You can also get Verilog, but
| that is usually used for verification instead of taping out the
| peripheral. This is because the company you buy the IP from
| will tape out the design for a specific node in order to
| guarantee the specifications. Examples of this would be
| everything from arm cores to uart and spi controllers as well
| as pretty much anything you could buy as a standalone IC.
| alain94040 wrote:
| I agree with most of the technical points of the article.
|
| But there may still be value in YC calling for innovation in that
| space. The article is correctly showing that there is no easy win
| in applying LLMs to chip design. Either the market for a given
| application is too small, then LLMs can help but who cares, or
| the chip is too important, in which case you'd rather use the
| best engineers. Unlike software, we're not getting much of a long
| tail effect in chip design. Taping out a chip is just not
| something a hacker can do, and even playing with an FPGA has a
| high cost of entry compared to hacking on your PC.
|
| But if there was an obvious path forward, YC wouldn't need to ask
| for an innovative approach.
| alw4s wrote:
| you could say it is the naive arrogance of the beginner mind.
|
| seen here as well when george-hotz attempts to overthow the
| chip companies with his plan for an ai chip
| https://geohot.github.io/blog/jekyll/update/2021/06/13/a-bre...
| little realizing the complexity involved. to his credit, he
| quickly pivoted into a software and tiny-box maker.
| jeltz wrote:
| > But if there was an obvious path forward, YC wouldn't need to
| ask for an innovative approach.
|
| How many experts do YC have on chip design?
| alain94040 wrote:
| I know several founders who went through YC in the chip
| design space, so even if the people running YC don't have a
| chip design background, just like VCs, they learn from
| hearing pitches of the founders who actually know the space.
| bubaumba wrote:
| > But if there was an obvious path forward
|
| Even obvious can be risky. First it's nice to share the risk,
| second more investments come with more connections.
|
| As for LLMs boom. I think finally we'll realize that LLM with
| algorithms can do much more than just LLM. 'algorithms' is
| probably a bad word here, I mean assisting tools like
| databases, algorithms, other models. Then only access API can
| be trained into LLM instead of the whole dataset for example.
| Neywiny wrote:
| I think this whole article is predicated on misinterpreting the
| ask. It wasn't for the chip to take 100x less power, it was for
| the algorithm the chip implements. Modern synthesis tools and
| optimisers extensively look for design patterns the same way
| software compilers do. That's why there's recommended inference
| patterns. I think it's not impossible to expect an LLM to expand
| the capture range of these patterns to maybe suboptimal HDL. As a
| simple example, maybe a designer got really turned around and is
| doing some crazy math, and the LLM can go "uh, that's just
| addition my guy, I'll fix that for you."
| eight_ender wrote:
| Was surprised this comment was this far down. I re-read the YC
| ask three times to make sure I wasn't crazy. Dude wrote the
| whole article based on a misunderstanding.
| Neywiny wrote:
| Thanks... I had more points earlier but I guess people
| changed their mind and decided they liked it better his way
| idk
| EgoIncarnate wrote:
| The article seems to be be based on the current limitations of
| LLMs. I don't think YC and other VCs are betting on what LLMs can
| do today, I think they are betting on what they might be able to
| do in the future.
|
| As we've seen in the recent past, it's difficult to predict what
| the possibilities are for LLMS and what limitations will hold.
| Currently it seems pure scaling won't be enough, but I don't
| think we've reached the limits with synthetic data and reasoning.
| DeathArrow wrote:
| >The article seems to be be based on the current limitations of
| LLMs. I don't think YC and other VCs are betting on what LLMs
| can do today, I think they are betting on what they might be
| able to do in the future.
|
| Do we know what LLMs will be able to do in the future? And even
| if we know, the startups have to work with what they have now,
| until that future comes. The article states that there's not
| much to work with.
| brookst wrote:
| Show me a successful startup that was predicated on the tech
| they're working with _not_ advancing?
| jeltz wrote:
| Most? I can list tens of them easily. For example what
| advancements were required for Slack to be successful? Or
| Spotify (they got more successful due to smartphones and
| cheaper bandwidth but the business was solid before that)?
| Or Shopify?
| brookst wrote:
| Slack bet on ubiquitous, continuous internet access.
| Spotify bet on bandwidth costs falling to effectively
| zero. Shopify bet on D2C rising because improved search
| engines, increased internet shopping (itself a result of
| several tech trends plus demographic changes).
|
| For a counterexample I think I'd look to non-tech
| companies. OrangeTheory maybe?
| talldayo wrote:
| Every single software service that has ever provided an
| Android or iOS application, for starters.
| rsynnott wrote:
| Most successful startups were able to make the thing that
| they wanted to make, as a startup, with existing tech. It
| might have a limited market that was expected to become
| less limited (a web app in 1996, say), but it was possible
| to make the thing.
|
| This idea of "we're a startup; we can't actually make
| anything useful now, but once the tech we use becomes magic
| any day now we might be able to make something!" is
| basically a new phenomenon.
| teamonkey wrote:
| The notion of a startup gaining funding to develop a
| fantasy into reality is relatively new.
|
| It used to be that startups would be created to do
| something different with existing tech or to commercialise
| a newly-discovered - but real - innovation.
| kokanee wrote:
| Tomorrow, LLMs will be able to perform slightly below-average
| versions of whatever humans are capable of doing tomorrow.
| Because they work by predicting what a human would produce
| based on training data.
| herval wrote:
| This severely discounts the fact that you're comparing a
| model that _knows the average about everything_ to a single
| human's capabilit. Also they can do it instantly, instead of
| having to coordinate many humans over long periods of time.
| You can't straight up compare one LLM to one human
| namaria wrote:
| "Knows the average relationship amongst all words in the
| training data" ftfy
| herval wrote:
| it seems that's sufficient to do a lot of things better
| than the average human - including coding, writing,
| creating poetry, summarizing and explaining things...
| namaria wrote:
| A human specialized in any of those things vastly
| outperforms the average human let alone an LLM.
| steveBK123 wrote:
| It's worth considering
|
| 1) all the domains there is no training data
|
| Many professions are far less digital than software, protect
| IP more, and are much more akin to an apprenticeship system.
|
| 2) the adaptability of humans in learning vs any AI
|
| Think about how many years we have been trying to train cars
| to drive, but humans do it with a 50 hours training course.
|
| 3) humans ability to innovate vs AIs ability to replicate
|
| A lot of creative work is adaptation, but humans do far more
| than that in synthesizing different ideas to create
| completely new works. Could an LLM produce the 37th Marvel
| movie? Yes probably. Could an LLM create.. Inception?
| Probably not.
| KaiserPro wrote:
| > I think they are betting on what they might be able to do in
| the future.
|
| Yeah, blind hope and a bit of smoke and lighting.
|
| > but I don't think we've reached the limits with synthetic
| data
|
| Synthetic data, at least for visual stuff can, in some cases
| provide the majority of training data. For $work, we can have
| say 100k video sequences to train a model, they can then be
| fine tuned on say 2k real videos. That gets it to be slightly
| under the same quality as if it was train on pure real video.
|
| So I'm not that hopeful that synthetic data will provide a
| breakthrough.
|
| I think the current architecture of LLMs are the limitation.
| They are fundamentally a sequence machine and are not capable
| of short, or medium term learning. context windows kinda makes
| up for that, but it doesn't alter the starting state of the
| model.
| layer8 wrote:
| You could replace "LLM" in your comment with lots of other
| technologies. Why bet on LLMs in particular to escape their
| limitations in the near term?
| samatman wrote:
| Because YCombinator is all about r-selecting startup ideas,
| and making it back on a few of them generating totally
| outsized upside.
|
| I think that LLMs are plateauing, but I'm less confident that
| this necessarily means the capabilities we're using LLMs for
| right now will also plateau. That is to say it's distinctly
| possible that all the talent and money sloshing around right
| now will line up a new breakthrough architecture in time to
| keep capabilities marching forward at a good pace.
|
| But if I had $100 million, and could bet $200 thousand that
| someone can make me billions on machine learning chip design
| or whatever, I'd probably entertain that bet. It's a numbers
| game.
| namaria wrote:
| > But if I had $100 million, and could bet $200 thousand
| that someone can make me billions on machine learning chip
| design or whatever, I'd probably entertain that bet. It's a
| numbers game.
|
| Problem with this reasoning is twofold: start-ups will
| overfit to getting your money instead of creating real
| advances; competition amongst them will drive up the
| investment costs. Pretty much what has been happening.
| yarri wrote:
| Please don't do this, Zach. We need to encourage more investment
| in the overall EDA market not less. Garry's pitch is meant for
| the dreamers, we should all be supportive. It's a big boat.
|
| Would appreciate the collective energy being spent instead
| towards adding to amor refining Garry's request.
| fsndz wrote:
| They want to throw LLMs at everything even if it does not make
| sense. Same is true for all the AI agent craze:
| https://medium.com/thoughts-on-machine-learning/langchains-s...
| marcosdumay wrote:
| If feels like the entire world has gone crazy.
|
| Even the serious idea that the article thinks could work is
| throwing the unreliable LLMs at _verification_! If there 's any
| place you can use something that doesn't work most of the time,
| I guess it's there.
| ajuc wrote:
| It's similar in regular programming - LLMs are better at
| writing test code than actual code. Mostly because it's
| simpler (P vs NP etc), but I think also because it's less
| obvious when test code doesn't work.
|
| Replace all asserts with expected ==expected and most people
| won't notice.
| jeltz wrote:
| > Replace all asserts with expected ==expected and most
| people won't notice.
|
| Those tests were very common back when I used to work in
| Ruby on Rails and automatically generating test stubs was a
| popular practice. These stubs were often just converted
| into expected == expected tests so that they passed and
| then left like that.
| majormajor wrote:
| LLMs are pretty damn useful for generating tests, getting
| rid of a lot of tedium, but yeah, it's the same as human-
| written tests: if you don't check that your test _doesn 't_
| work when it _shouldn 't_ (not the same thing as just
| writing a second test for that case - both those tests need
| to fail if you intentionally screw with their separate
| fixtures), then you shouldn't have too much confidence in
| your test.
| marcosdumay wrote:
| If LLMs can generate a test for you, it's because it's a
| test that you shouldn't need to write. They can't test
| what is really important, at all.
|
| Some development stacks are extremely underpowered for
| code verification, so they do patch the design issue.
| Just like some stacks are underpowered for abstraction
| and need patching by code generation. Both of those solve
| an immediate problem, in a haphazard and error-prone way,
| by adding burden on maintenance and code evolution
| linearly to how much you use it.
|
| And worse, if you rely too much on them they will lead
| your software architecture and make that burden
| superlinear.
| williamcotton wrote:
| Claude wrote the harness and pretty much all of these
| tests, eg:
|
| https://github.com/williamcotton/search-input-
| query/blob/mai...
|
| It is a good test suite and it saved me quite a bit of
| typing!
|
| In fact, Claude did most of the typing for the entire
| project:
|
| https://github.com/williamcotton/search-input-query
|
| BTW, I obviously didn't just type "make a lexer and
| multi-pass parser that returns multiple errors and then
| make a single-line instance of a Monaco editor with error
| reporting, type checking, syntax highlighting and tab
| completion".
|
| I put it together piece-by-piece and with detailed
| architectural guidance.
| MichaelNolan wrote:
| > Replace all asserts with expected == expected and most
| people won't notice.
|
| It's too resource intensive for all code, but mutation
| testing is pretty good at finding these sorts of tests that
| never fail. https://pitest.org/
| rsynnott wrote:
| I mean, define 'better'. Even with actual human
| programmers, tests which do not in fact test the thing are
| already a bit of an epidemic. A test which doesn't test is
| worse than useless.
| deadbabe wrote:
| This is typical of any hype bubble. Blockchain used to be the
| answer to everything.
| Mistletoe wrote:
| What's after this? Because I really do feel the economy is
| standing on a cliff right now. I don't see anything after
| this that can prop stocks up.
| deadbabe wrote:
| The post-quantum age. Companies will go post-quantum.
| namaria wrote:
| I think the operators are learning how to hype-edge. You
| find that sweet spot between promising and 'not just
| there yet' where you can take lots of investments and
| iterate forward just enough to keep it going.
|
| It doesn't matter if it can't actually 'get there' as
| long as people still believe it can.
|
| Come to think about it, a socioeconomic system dependent
| on population and economic growth is at a fundamental
| level driven by this balancing act: "We can solve every
| problem if we just forge ahead and keep enlarging the
| base of the pyramid - keep reproducing, keep investing,
| keep expanding the infrastructure".
| dgfitz wrote:
| That's because we are still waiting for the 2008 bubble
| to pop, which was inflated by the 2020 bubble. It's going
| to be bad. People will blame trump, Harris would be
| eating the same shit sandwich.
|
| It's gonna be bad.
| edmundsauto wrote:
| Only if it fails in the same way. LLMs and the multi-agent
| approach operate under the assumption that they are
| programmable agents and each agent is more of a trade off
| against failure modes. If you can string them together, and
| if the output is easily verified, it can be a great fit for
| the problem.
| FredPret wrote:
| This happens all the time.
|
| Once it was spices. Then poppies. Modern art. The .com craze.
| Those blockchain ape images. Blockchain. Now LLM.
|
| All of these had a bit of true value and a whole load of
| bullshit. Eventually the bullshit disappears and the core
| remains, and the world goes nuts about the next thing.
| vishnugupta wrote:
| Exactly. I've seen this enough now to appreciate that oft
| repeated tech adoption curve. It seems like we are in "peak
| expectations" phase which is immediately followed by the
| disillusionment and then maturity phase.
| cwzwarich wrote:
| If your LLM is producing a proof that can be checked by
| another program, then there's nothing wrong with their
| reliability. It's just like playing a game whose rules are a
| logical system.
| ReptileMan wrote:
| Isn't that the case with every new tech. There was a time in
| which people tried to cook everything in a microwave
| ksynwa wrote:
| Microwave sellers did not become trillion dollar companies
| off that hype
| ReptileMan wrote:
| Mostly because the marginal cost of microwaves was not
| close to zero.
| ksynwa wrote:
| Mostly because they were not making claims that sentient
| microwaves that would cook your food for you were just
| around the corner which then the most respected media
| outlets parroted uncritically.
| rsynnott wrote:
| I mean, they were at one point making pretty extravagant
| claims about microwaves, but to a less credulous
| audience. Trouble with LLMs is that they look like magic
| if you don't look too hard, particularly to laypeople.
| It's far easier to buy into a narrative that they
| actually _are_ magic, or will become so.
| lxgr wrote:
| I feel like what makes this a bit different from just
| regular old sufficiently advanced technology is the
| combination of two things:
|
| - LLMs are extremely competent at surface-level pattern
| matching and manipulation of the type we'd previously
| assumed that only AGI would be able to do.
|
| - A large fraction of tasks (and by extension jobs) that
| we used to, and largely still do, consider to be
| "knowledge work", i.e. requiring a high level of skill
| and intelligence, are in fact surface-level pattern
| matching and manipulation.
|
| Reconciling these facts raises some uncomfortable
| implications, and calling LLMs "actually intelligent"
| lets us avoid these.
| Karrot_Kream wrote:
| Even rice cookers started doing this by advertising
| "fuzzy logic".
| AlotOfReading wrote:
| Fuzzy logic rice cookers are the result of an unrelated
| fad in 1990s Japanese engineering companies. They added
| fuzzy controls to everything from cameras to subways to
| home appliances. It's not part of the current ML fad.
| namaria wrote:
| When did OpenMicroWave promise to solve every societal
| problem if we just gave it enough money to built a larger
| microwave oven?
| xbmcuser wrote:
| yes thats how we progress this is how the internet boom
| happened as well everything became . com then the real workable
| businesses were left and all the unworkable things were gone.
|
| Recently I came across some one advertising an LLM to generate
| fashion magazine shoot in Pakistan at 20-25% of the cost. It
| hit me then that they are undercutting the fashion shoot of
| country like Pakistan which is already cheaper by 90-95% from
| most western countries. This AI is replacing the work of 10-20
| people.
| startupsfail wrote:
| The annoying part, a lot of money could be funneled into
| these unworkable businesses in the process, crypto being a
| good example. And these unworkable businesses tend to try to
| continue getting their way into the money somehow regardless.
| Most recent example was funneling money from Russia into
| Trump's campaign.
| bubaumba wrote:
| > The annoying part, a lot of money could be funneled into
| these unworkable businesses in the process, crypto being a
| good example
|
| There was a thread here about why ycombinator invests into
| several competing startups. The answer is success is often
| more about connections and politics than the product
| itself. And crypto, yes, is a good example of this. Musk
| will get his $1B in bitcoins back for sure.
|
| > Most recent example was funneling money from Russia into
| Trump's campaign.
|
| Musk again?
| alw4s wrote:
| please dont post a link that is behind a paywall !!
| ksynwa wrote:
| https://archive.is/dLp6t
|
| It is a registration wall I think.
| tomrod wrote:
| Same result. Information locks are verboten.
| lxgr wrote:
| As annoying as I find them, on this site they're in fact
| not: https://news.ycombinator.com/item?id=10178989
| lxgr wrote:
| Please don't complain about paywalls:
| https://news.ycombinator.com/item?id=10178989
| wslh wrote:
| This makes complete sense from an investor's perspective, as it
| increases the chances of a successful exit. While we focus on
| the technical merits or critique here on HN/YC, investors are
| playing a completely different game.
|
| To be a bit acerbic, and inspired by Arthur C. Clarke, I might
| say: "Any sufficiently complex business could be
| indistinguishable from Theranos".
| spencerchubb wrote:
| Theranos was not a "complex business". It was deliberate
| fraud and deception, and investors that were just gullible.
| The investors should have demanded to see concrete results
| wslh wrote:
| I expected you to take this with a grain of salt but also
| to read between the lines: while some projects involve
| deliberate fraud, others may simply lack coherence and
| inadvertently follow the principles of the greater fool
| theory [1]. The use of ambiguous or indistinguishable
| language often blurs the distinction, making it harder to
| differentiate outright deception from an unsound business
| model.
|
| [1] https://en.wikipedia.org/wiki/Greater_fool_theory
| trolan wrote:
| https://archive.ph/dLp6t
| rsynnott wrote:
| It really feels like we're close to the end of the current
| bubble now; the applications being trotted out are just
| increasingly absurd.
| spencerchubb wrote:
| LLMs have powered products used by hundreds of millions, maybe
| billions. Most experiments will fail and that's okay, arguably
| even a good thing. Only time will tell which ones succeed
| logifail wrote:
| > They want to throw LLMs at everything [..]
|
| Oh yes.
|
| I had a discussion with a manager at a client last week and was
| trying to run him through some (technical) issues relating to
| challenges an important project faces.
|
| His immediate response was that maybe we should just let
| ChatGPT help us decide the best option. I had to bite my
| tongue.
|
| OTOH, I'm more and more convinced that ChatGPT will replace
| managers long before it replaces technical staff.
| isoprophlex wrote:
| > I knew it was bullshit from the get-go as soon as I read
| their definition of AI agents.
|
| That is one spicy article, it got a few laughs out of me. I
| must agree 100% that Langchain is an abomination, both their
| APIs as well as their marketing.
| klabb3 wrote:
| 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...
| childintime wrote:
| You mean, like humans have been for many decades now.
|
| Edit: I believe that LLM's are eminently useful to replace
| experts (of all people) 90% of the time.
| datameta wrote:
| Change "replace" to "supplement" and I agree. The level of
| non-determinism is just too great at this stage, imo.
| beepbooptheory wrote:
| I don't know if they "eminently" anything at the moment,
| thats why you feel the need to make the comment, right?
| layer8 wrote:
| People believed that about expert systems in the 1980s as
| well.
| majormajor wrote:
| > Edit: I believe that LLM's are eminently useful to replace
| experts (of all people) 90% of the time.
|
| What do you mean by "expert"?
|
| Do you mean the pundit who goes on TV and says "this policy
| will be bad for the economy"?
|
| Or do you mean the seasoned developer who you hire to fix
| your memory leaks? To make your service fast? Or cut your
| cloud bill from 10M a year to 1M a year?
| lxgr wrote:
| Experts of the kind that will be able to talk for hours about
| the academic consensus on the status quo without once
| considering how the question at hand might challenge it?
| Quite likely.
|
| Experts capable of critical thinking and reflecting on
| evidence that contradicts their world model (and thereby
| retraining it on the fly)? Most likely not, at least not in
| their current architecture with all its limitations.
| brookst wrote:
| You find it hard to believe that non-deterministic black boxes
| at the core of complex systems are eager to put non-
| deterministic black boxes at the core of complex systems?
| beepbooptheory wrote:
| Can you actually like follow through with this line? I know
| there are literally tens of thousands of comments just like
| this at this point, but if you have chance, could you explain
| what _you_ think this means? What should we take from it?
| Just unpack it a little bit for us.
| therealcamino wrote:
| I took it to be a joke that the description "slow,
| expensive, non-deterministic black boxes" can apply to the
| engineers themselves. The engineers would be the ones who
| would have to place LLMs at the core of the system. To
| anyone outside, the work of the engineers is as opaque as
| the operation of LLMs.
| croshan wrote:
| An interpretation that makes sense to me: humans are non-
| deterministic black boxes already at the core of complex
| systems. So in that sense, replacing a human with AI is not
| unreasonable.
|
| I'd disagree, though: humans are still easier to predict
| and understand (and trust) than AI, typically.
| sdesol wrote:
| With humans we have a decent understanding of what they
| are capable of. I trust a medical professional to provide
| me with medical advice and an engineer to provide me with
| engineering advice. With LLM, it can be unpredictable at
| times, and they can make errors in ways that you would
| not imagine. Take the following examples from my tool,
| which shows how GPT-4o and Claude 3.5 Sonnet can screw
| up.
|
| In this example, GPT-4o cannot tell that GitHub is
| spelled correctly:
|
| https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samp
| les...
|
| In this example, Claude cannot tell that GitHub is
| spelled correctly:
|
| https://app.gitsense.com/?doc=905f4a9af74c25f&model=Claud
| e+3...
|
| I still believe LLM is a game changer and I'm currently
| working on what I call a "Yes/No" tool which I believe
| will make trusting LLMs a lot easier (for certain things
| of course). The basic idea is the "Yes/No" tool will let
| you combine models, samples and prompts to come to a Yes
| or No answer.
|
| Based on what I've seen so far, a model can easily screw
| up, but it is unlikely that all will screw up at the same
| time.
| visarga wrote:
| It's actually a great topic - both humans and LLMs are
| black boxes. And both rely on patterns and abstractions
| that are leaky. And in the end it's a matter of trust,
| like going to the doctor.
|
| But we have had extensive experience with humans, it is
| normal to have better defined trust, LLMs will be better
| understood as well. There is no central understander or
| truth, that is the interesting part, it's a "Blind men
| and the elephant" situation.
| sdesol wrote:
| We are entering the nondeterministic programming era in
| my opinion. LLM applications will be designed with the
| idea that we can't be 100% sure and what ever solution
| can provide the most safe guards, will probably be the
| winner.
| brookst wrote:
| Sure. I mean, humans are very good at building businesses
| and technologies that are resilient to human fallibility.
| So when we think of applications where LLMs might replace
| or augment humans, it's unsurprising that their fallible
| nature isn't a showstopper.
|
| Sure, EDA _tools_ are deterministic, but the humans who
| apply them are not. Introducing LLMs to these processes is
| not some radical and scary departure, it's an iterative
| evolution.
| beepbooptheory wrote:
| Ok yeah. I think the thing that trips me up with this
| argument then is just, yes, when you regard humans in a
| certain neuroscientific frame and consider things like
| consciousness or language or will, they are fundamentally
| nondeterministic. But that isn't the frame of mind of the
| human engineer who _does_ the work or even validates it.
| When the engineer is working, they aren 't seeing
| themselves as some black box which they must feed input
| and get output, they are thinking about the things in
| themselves, justifying to themselves and others their
| work. Just because you can place yourself in some
| hypothetical third person here, one that oversees the
| model and the human and says "huh yeah they are pretty
| much the same, huh?", doesn't actually tell us anything
| about whats _happening_ on the ground in either case, if
| you will. At the very least, this same logic would imply
| fallibility is one dimensional and always statistical;
| "the patient may be dead, but at least they got a new
| heart." Like isn't in important to be in love, not just
| be married? To borrow some Kant, shouldn't we still value
| what we can do when we think _as if_ we aren 't just some
| organic black box machines? Is there even a question
| there? How could it be otherwise?
|
| Its really just that the "in principle" part of the
| overall implication with your comment and so many others
| just doesn't make sense. Its very much cutting off your
| nose to spite your face. How could science itself be
| possible, much less engineering, if this is how we
| decided things? If we regarded ourselves always from the
| outside? How could even be motivated to debate whether we
| get the computers to design their own chips? When would
| something actually _happen_? At some point, people _do_
| have ideas, in a full, if false, transparency to
| themselves, that they can write down and share and
| explain. This is not only the thing that has gotten us
| this far, it is the very essence of _why_ these models
| are so impressive in the certain ways that they are. It
| doesn 't make sense to argue for the fundamental
| cheapness of the very thing you are ultimately trying to
| defend. And it imposes this strange perspective where we
| are not even living inside our own (phenomenal) minds
| anymore, that it fundamentally _never matters_ what we
| think, no matter our justification. Its weird!
|
| I'm sure you have a lot of good points and stuff, I just
| am simply pointing out that this particular argument is
| maybe not the strongest.
| og_kalu wrote:
| Because people are not saying "let's replace Casio
| Calculators with interfaces to GPT!"
|
| By and large, the processes people are scrambling to place
| LLMs in are ones that typical machines struggle or fail and
| humans excel or do decently (and that LLMs are making some
| headway in).
|
| There's no point comparing LLM performance to some
| hypothetical perfect understanding machine that doesn't
| exist. It's nonsensical actually. You compare it to the
| performance of the beings it's meant to replace or augment
| - humans.
|
| Replacing non-deterministic black boxes with potentially
| better performing non-deterministic black boxes is not some
| crazy idea.
| klabb3 wrote:
| Yes I do! Is that some sort of gotcha? 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",
| I'm going to pick the script. Who wouldn't? Until machines
| can reliably understand, operate and self-correct
| independently, I'd rather not give up debuggability and
| understandability.
| og_kalu wrote:
| >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 that
| typical machines struggle or fail and humans excel or do
| decently (and that LLMs are making some headway in).
|
| You're making the wrong distinction here. It's not Dave vs
| your nifty script. It's Dave or nothing at all.
|
| There's no point comparing LLM performance to some
| hypothetical perfect understanding machine that doesn't
| exist.
|
| You compare to the things its meant to replace - humans.
| How well can the LLM do this compared to Dave ?
| kuhewa wrote:
| > 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)
| OkGoDoIt wrote:
| I think this comment and the parent comment are talking
| about two different things. One of you is talking about
| using nondeterministic ML to implement the actual core
| logic (an automated script or asking Dave to do it
| manually), and one of you is talking about using it to
| design the logic (the equivalent of which is writing that
| automated script).
|
| LLM's are not good at actually doing the processing, they
| are not good at math or even text processing at a character
| level. They often get logic wrong. But they are pretty good
| at looking at patterns and finding creative solutions to
| new inputs (or at least what can appear creative, even if
| philosophically it's more pattern matching than
| creativity). So an LLM would potentially be good at writing
| a first draft of that script, which Dave could then
| proofread/edit, and which a standard deterministic computer
| could just run verbatim to actually do the processing.
| Eventually maybe even Dave's proofreading would be
| superfluous.
|
| Tying this back to the original article, I don't think
| anyone is proposing having an LLM inside a chip that
| processes incoming data in a non-deterministic way. The
| article is about using AI to design the chips in the first
| place. But the chips would still be deterministic, the
| equivalent of the script in this analogy. There are plenty
| of arguments to make about LLM's not being good enough for
| that, not being able to follow the logic or optimize it, or
| come up with novel architectures. But the shape of chip
| design/Verilog feels like something that with enough
| effort, an AI could likely be built that would be pretty
| good at it. All of the knowledge that those smart
| knowledgeable engineers which are good at writing Verilog
| have built up can almost certainly be represented in some
| AI form, and I wouldn't bet against AI getting to a point
| where it can be helpful similarly to how Copilot currently
| is with code completion. Maybe not perfect anytime soon,
| but good enough that we could eventually see a path to
| 100%. It doesn't feel like there's a fundamental reason
| this is impossible on a long enough time scale.
| klabb3 wrote:
| > So an LLM would potentially be good at writing a first
| draft of that script, which Dave could then
| proofread/edit
|
| Right, and there's nothing fundamentally wrong with this,
| nor is it a novel method. We've been joking about copying
| code from stack overflow for ages, but at least we didn't
| pretend that it's the peak of human achievement. Ask a
| teacher the difference between writing an essay and
| proofreading it.
|
| Look, my entire claim from the beginning is that
| understanding is important (epistemologically, it may be
| what separates engineering from alchemy, but I digress).
| Practically speaking, if we see larger and larger pieces
| of LLM written code, it will be similar to Dave and his
| incomprehensible VBA script. It works, but nobody knows
| why. Don't get me wrong, this isn't new at all. It's an
| ever-present wet blanket that slowly suffocates
| engineering ventures who don't pay attention and actively
| resist. In that context, uncritically inviting a second
| wave of monkeys to the nuclear control panels, that's
| what baffles me.
| crabmusket wrote:
| > We've been joking about copying code from stack
| overflow for ages
|
| Tangent for a slight pet peeve of mine:
|
| "We" did joke about this, but probably because most of
| our jobs are not in chip design. "We" also know the
| limits of this approach.
|
| The fact that Stack Overflow is the most SEO optimised
| result for "how to center div" (which we always forget
| how to do) doesn't have any bearing on the times when we
| have an actual problem requiring our attention and
| intellect. Say diagnosing a performance issue,
| negotiating requirements and how they subtly differ in an
| edge case from the current system behaviour, discovering
| a shared abstraction in 4 pieces of code that are nearly
| but not quite the same.
|
| I agree with your posts here, the Stack Overflow thing in
| general is just a small hobby horse I have.
| talldayo wrote:
| In a reductive sense, this passage might as well read "You
| find it hard to believe that entropy is the source of other
| entropic reactions?"
|
| No, I'm just disappointed in the decision of Black Box A and
| am bound to be even more disappointed by Black Box B. If we
| continue removing thoughtful design from our systems because
| thoughtlessness is the default, nobody's life will improve.
| ithkuil wrote:
| I'm a non-deterministic black box who teaches complex
| deterministic machines to do stuff and leverages other
| deterministic machines as tools to do the job.
|
| I like my job.
|
| My job also involves cooperating with other non-deterministic
| black boxes (colleagues).
|
| I can totally see how artificial non-deterministic black
| boxes (artificial colleagues) may be useful to
| replace/augment the biological ones.
|
| For one, artificial colleagues don't get tired and I don't
| accidentally hurt their feelings or whatnot.
|
| In any case, I'm not looking forward to replacing my
| deterministic tools with the fuzzy AI stuff.
|
| Intuitively at least it seems to me that these non-
| deterministic black boxes could really benefit from using the
| deterministic tools for pretty much the same reasons we do as
| well.
| xg15 wrote:
| Yes. One does not have to do with the other.
| wslh wrote:
| 100% agree. While I can't find all the sources right now, [1]
| and its references could be a good starting point for further
| exploration. I recall there being a proof or conjecture
| suggesting that it's impossible to build an "LLM firewall"
| capable of protecting against all possible prompts--though my
| memory might be failing me
|
| [1] https://arxiv.org/abs/2410.07283
| tucnak wrote:
| The "naive", all-or-nothing view on LLM technology is, frankly,
| more tiring than the hype.
| lolinder wrote:
| One of the consistent problems I'm seeing over and over again
| with LLMs is people forgetting that they're limited by the
| training data.
|
| Software engineers get hyped when they see the progress in AI
| coding and immediately begin to extrapolate to other fields--if
| Copilot can reduce the burden of coding so much, think of all the
| money we can make selling a similar product to XYZ industries!
|
| The problem with this extrapolation is that the software industry
| is pretty much unique in the amount of information about its
| inner workings that is publicly available for training on. We've
| spent the last 20+ years writing millions and millions of lines
| of code that we published on the internet, not to mention
| answering questions on Stack Overflow (which still has 3x as many
| answers as all other Stack Exchanges combined [0]), writing
| technical blogs, hundreds of thousands of emails in public
| mailing lists, and so on.
|
| Nearly every other industry (with the possible exception of Law)
| produces publicly-visible output at a tiny fraction of the rate
| that we do. Ethics of the mass harvesting aside, it's simply _not
| possible_ for an LLM to have the same skill level in ${insert
| industry here} as they do with software, so you _can 't_
| extrapolate from Copilot to other domains.
|
| [0] https://stackexchange.com/sites?view=list#answers
| mountainriver wrote:
| Yep, this is also the reason LLMs can probably work well for a
| lot more things if we did have the data
| unoti wrote:
| >The problem with this extrapolation is that the software
| industry is pretty much unique in the amount of information
| about its inner workings that is publicly available for
| training on... millions of lines of code that we published on
| the internet...
|
| > Nearly every other industry (with the possible exception of
| Law) produces publicly-visible output at a tiny fraction of the
| rate that we do.
|
| You are correct! There's lots of information available publicly
| about certain things like code, and writing SQL queries. But
| other specialized domains don't have the same kind of
| information trained into the heart of the model.
|
| But importantly, this doesn't mean the LLM can't provide
| significant value in these other more niche domains. They still
| can, and I provide this every day in my day job. But it's a lot
| of work. We (as AI engineers) have to deeply understand the
| special domain knowledge. The basic process is this:
|
| 1. Learn how the subject matter experts do the work.
|
| 2. Teach the LLM to do this, using examples, giving it
| procedures, walking it through the various steps and giving it
| the guidance and time and space to think. (Multiple prompts,
| recipes if you will, loops, external memory...)
|
| 3. Evaluation, iteration, improvement
|
| 4. Scale up to production
|
| In many domains I work in, it can be very challenging to get
| past step 1. If I don't know how to do it effectively, I can't
| guide the LLM through the steps. Consider an example question
| like "what are the top 5 ways to improve my business" -- the
| subject matter experts often have difficulty teaching me how to
| do that. If they don't know how to do it, they can't teach it
| to me, and I can't teach it to the agent. Another example that
| will resonate with nerds here is being an effective Dungeons
| and Dragons DM. But if I actually learn how to do it, and boil
| it down into repeatable steps, and use GraphRAG, then it
| becomes another thing entirely. I know this is possible, and
| expect to see great things in that space, but I estimate it'll
| take another year or so of development to get it done.
|
| But in many domains, I get access to subject matter experts
| that can tell me pretty specifically how to succeed in an area.
| These are the top 5 situations you will see, how you can
| identify which situation type it is, and what you should do
| when you see that you are in that kind of situation. In domains
| like this I can in fact make the agent do awesome work and
| provide value, even when the information is not in the publicly
| available training data for the LLM.
|
| There's this thing about knowing a domain area well enough to
| do the job, but not having enough mastery to teach others how
| to do the job. You need domain experts that understand the job
| well enough to teach you how to do it, and you as the AI
| engineer need enough mastery over the agent to teach it how to
| do the job as well. Then the magic happens.
|
| When we get AGI we can proceed past this limitation of needing
| to know how to do the job ourselves. Until we get AGI, then
| this is how we provide impact using agents.
|
| This is why I say that even if LLM technology does not improve
| any more beyond where it was a year ago, we still have many
| years worth of untapped potential for AI. It just takes a lot
| of work, and most engineers today don't understand how to do
| that work-- principally because they're too busy saying today's
| technology can't do that work rather than trying to learn how
| to do it.
| akra wrote:
| > 1. Learn how the subject matter experts do the work.
|
| This will get harder I think over time as low hanging fruit
| domains are picked - the barrier will be people not
| technology. Especially if the moat for that domain/company is
| the knowledge you are trying to acquire (NOTE: Some
| industries that's not their moat and using AI to shed more
| jobs is a win). Most industries that don't have public
| workings on the internet have a couple of characteristics
| that will make it extremely difficult to perform Task 1 on
| your list. The biggest is now every person on the street,
| through the mainstream news, etc knows that it's not great to
| be a software engineer right now and most media outlets point
| straight to "AI". "It's sucks to be them" I've heard people
| say - what was once a profession of respect is now "how long
| do you think you have? 5 years? What will you do instead?".
|
| This creates a massive resistance/outright potential lies in
| providing AI developers information - there is a precedent of
| what happens if you do and it isn't good for the
| person/company with the knowledge. Doctors associations,
| apprenticeship schemes, industry bodies I've worked with are
| all now starting to care about information security a lot
| more due to "AI", and proprietary methods of working lest AI
| accidentally "train on them". Definitely boosted the demand
| for cyber people again as an example around here.
|
| > You are correct! There's lots of information available
| publicly about certain things like code, and writing SQL
| queries. But other specialized domains don't have the same
| kind of information trained into the heart of the model.
|
| The nightmare of anyone that studied and invested into a
| skill set according to most people you would meet. I think
| most practitioners will conscious to ensure that the lack of
| data to train on stays that way for as long as possible -
| even if it eventually gets there the slower it happens and
| the more out of date it is the more useful the human
| skill/economic value of that person. How many people would of
| contributed to open source if they knew LLM's were coming for
| example? Some may have, but I think there would of been less
| all else being equal. Maybe quite a bit less code to the
| point that AI would of been delayed further - tbh if Google
| knew that LLM's could scale to be what they are they wouldn't
| of let that "attention" paper be released either IMO.
| Anecdotally even the blue collar workers I know are now
| hesitant to let anyone near their methods of working and
| their craft - survival, family, etc come first. In the end
| after all, work is a means to an end for most people.
|
| Unlike us techies which I find at times to not be "rational
| economic actors" many non-tech professionals don't see AI as
| an opportunity - they see it as a threat they they need to
| counter. At best they think they need to adopt AI, before
| others have it and make sure no one else has it. People I've
| chatted to say "no one wants this, but if you don't do it
| others will and you will be left behind" is a common
| statement. One person likened it to a nuclear weapons arms
| race - not a good thing, but if you don't do it you will be
| under threat later.
| aleph_minus_one wrote:
| > This will get harder I think over time as low hanging
| fruit domains are picked - the barrier will be people not
| technology. Especially if the moat for that domain/company
| is the knowledge you are trying to acquire (NOTE: Some
| industries that's not their moat and using AI to shed more
| jobs is a win).
|
| Also consider that there exist quite a lot of subject
| matter experts who simply are not AI fanboys - not because
| they are afraid of their job because of AI, but because
| they consider the whole AI hype to be insanely annoying and
| infuriating. To get them to work with an AI startup, you
| will thus have to pay them quite a lot of money.
| steveBK123 wrote:
| Yes this is EXACTLY it, and I was discussing this a bit at work
| (financial services).
|
| In software, we've all self taught, improved, posted Q&A all
| over the web. Plus all the open source code out there. Just
| mountains and mountains of free training data.
|
| However software is unique in being both well paying and
| something with freely available, complete information online.
|
| A lot of the rest of the world remains far more closed and
| almost an apprenticeship system. In my domain thinks like
| company fundamental analysis, algo/quant trading, etc. Lots of
| books you can buy from the likes of Dalio, but no real (good)
| step by step research and investment process information
| online.
|
| Likewise I'd imagine heavily patented/regulated/IP industries
| like chip design, drug design, etc are substantially as closed.
| Maybe companies using an LLM on their own data internally could
| make something of their data, but its also quite likely there
| is no 'data' so much as tacit knowledge handed down over time.
| paulsutter wrote:
| Generative models are bimodal - in certain tasks they are crazy
| terrible , and in certain tasks they are better than humans. The
| key is to recognize which is which.
|
| And much more important:
|
| - LLMs can suddenly become more competent when you give them the
| right tools, just like humans. Ever try to drive a nail without a
| hammer?
|
| - Models with spatial and physical awareness are coming and will
| dramatically broaden what's possible
|
| It's easy to get stuck on what LLMs are bad at. The art is to
| apply an LLMs strengths to your specific problem, often by
| augmenting the LLM with the right custom tools written in regular
| code
| necovek wrote:
| > Ever try to drive a nail without a hammer?
|
| I've driven a nail with a rock, a pair of pliers, a wrench,
| even with a concrete wall and who knows what else!
|
| I didn't need to be told if these can be used to drive a nail,
| and I looked at things available, looked for a flat surface on
| them and good grip, considered their hardness, and then simply
| used them.
|
| So if we only give them the "right" tools, they'll remain very
| limited by us not thinking about possible jobs they'll appear
| as if they know how to do and they don't.
|
| The problem is exactly that: they "pretend" to know how to
| drive a nail but not really.
| paulsutter wrote:
| Those are all tools !! Congratulations
|
| If you're creative enough to figure out different tools for
| humans, you are creative enough to figure out different tools
| for LLMs
| herval wrote:
| YC is just spraying & praying AI, like most investors
| B1FF_PSUVM wrote:
| And liable to make money at it, on a "greater fool" basis - a
| successful sale (exit) is not necessarily a successful,
| profitable company ...
| thrw42A8N wrote:
| In the case of YC, their stake is so low that they don't
| really get any upside unless it's a successful, profitable
| company.
| alw4s wrote:
| design automation tooling startups have it incredibly hard -
| first, customers wont buy from startups, and second, the space
| of possibly exits via acquisitions is tiny.
| spamizbad wrote:
| LLMs have a long way to go in the world of EDA.
|
| A few months ago I saw a post on LinkedIn where someone fed the
| leading LLMs a counter-intuitively drawn circuit with 3
| capacitors in parallel and asked what the total capacitance was.
| Not a single one got it correct - not only did they say the caps
| were in series (they were not) it even got the series capacitance
| calculations wrong. I couldn't believe they whiffed it and had to
| check myself and sure enough I got the same results as the author
| and tried all types of prompt magic to get the right answer... no
| dice.
|
| I also saw an ad for an AI tool that's designed to help you
| understand schematics. In its pitch to you, it's showing what
| looks like a fairly generic guitar distortion pedal circuit and
| does manage to correctly identify a capacitor as blocking DC but
| failed to mention it also functions as a component in an RC high-
| pass filter. I chuckled when the voice over proudly claims "they
| didn't even teach me this in 4 years of Electrical Engineering!"
| (Really? They don't teach how capacitors block DC and how RC
| filters work????)
|
| If you're in this space you probably need to compile your own
| carefully curated codex and train something more specialized. The
| general purpose ones struggle too much.
| MrsPeaches wrote:
| How many words in the art of electronics? Could you give that
| as context and see if might help?
| hinkley wrote:
| I still have nightmares about the entry level EE class I was
| required to take for a CS degree.
|
| RC circuits man.
| cruffle_duffle wrote:
| "Oh shit I better remember all that matrix algebra I forgot
| already!"
|
| ...Then takes a class on anything with 3d graphics... "oh
| shit matrix algebra again!"
|
| ...then takes a class on machine learning "urg more matrix
| math!"
| seanmcdirmid wrote:
| EEs actually had a head start on ML, especially those who
| took signal processing.
| crabmusket wrote:
| I studied mechatronics and did reasonably well... but in any
| electrical class I would just scrape by. I loved it but was
| apparently not suited to it. I remember a whole unit
| basically about transistors. On the software/mtrx side we
| were so happy treating MOSFETs as digital. Having to analyse
| them in more depth did my head in.
| mportela wrote:
| I had a similar experience, except Mechanical Engineering
| being my weakest area. Computer Science felt like a
| children's game compared to fluid dynamics...
| grepLeigh wrote:
| Nvidia is trying something similar:
| https://blogs.nvidia.com/blog/llm-semiconductors-chip-nemo/
|
| I'd want to know about the results of these experiments before
| casting judgement either way. Generative modeling has actual
| applications in the 3D printing/mechanical industry.
| westurner wrote:
| IDK about LLMs there either.
|
| A non-LLM monte carlo AI approach: "Pushing the Limits of Machine
| Design: Automated CPU Design with AI" (2023)
| https://arxiv.org/abs/2306.12456 ..
| https://news.ycombinator.com/item?id=36565671
|
| A useful target for whichever approach is most efficient at IP-
| feasible design:
|
| From https://news.ycombinator.com/item?id=41322134 :
|
| > _" Ask HN: How much would it cost to build a RISC CPU out of
| carbon?" (2024) https://news.ycombinator.com/item?id=41153490 _
| stefanpie wrote:
| This heavily overlaps with my current research focus for my
| Ph.D., so I wanted to provide some additional perspective to the
| article. I have worked with Vitis HLS and other HLS tools in the
| past to build deep learning hardware accelerators. Currently, I
| am exploring deep learning for design automation and using large
| language models (LLMs) for hardware design, including leveraging
| LLMs to write HLS code. I can also offer some insight from the
| academic perspective.
|
| First, I agree that the bar for HLS tools is relatively low, and
| they are not as good as they could be. Admittedly, there has been
| significant progress in the academic community to develop open-
| source HLS tools and integrations with existing tools like Vitis
| HLS to improve the HLS development workflow. Unfortunately,
| substantial changes are largely in the hands of companies like
| Xilinx, Intel, Siemens, Microchip, MathWorks (yes, even Matlab
| has an HLS tool), and others that produce the "big-name" HLS
| tools. That said, academia has not given up, and there is
| considerable ongoing HLS tooling research with collaborations
| between academia and industry. I hope that one day, some lab will
| say "enough is enough" and create a open-source, modular HLS
| compiler in Rust that is easy to extend and contribute to--but
| that is my personal pipe dream. However, projects like BambuHLS,
| Dynamatic, MLIR+CIRCT, and XLS (if Google would release more of
| their hardware design research and tooling) give me some hope.
|
| When it comes to actually using HLS to build hardware designs, I
| usually suggest it as a first-pass solution to quickly prototype
| designs for accelerating domain-specific applications. It
| provides a prototype that is often much faster or more power-
| efficient than a CPU or GPU solution, which you can implement on
| an FPGA as proof that a new architectural change has an advantage
| in a given domain (genomics, high-energy physics, etc.). In this
| context, it is a great tool for academic researchers. I agree
| that companies producing cutting-edge chips are probably not
| using HLS for the majority of their designs. Still, HLS has its
| niche in FPGA and ASIC design (with Siemens's Catapult being a
| popular option for ASIC flows). However, the gap between an
| initial, naive HLS design implementation and one refined by
| someone with expert HLS knowledge is enormous. This gap is why
| many of us in academia view the claim that "HLS allows software
| developers to do hardware development" as somewhat moot (albeit
| still debatable--there is ongoing work on new DSLs and
| abstractions for HLS tooling which are quite slick and
| promising). Because of this gap, unless you have team members or
| grad students familiar with optimizing and rewriting designs to
| fully exploit HLS benefits while avoiding the tools' quirks and
| bugs, you won't see substantial performance gains. Al that to
| say, I don't think it is fair to comply write off HLS as a lost
| cause or not sucesfull.
|
| Regarding LLMs for Verilog generation and verification, there's
| an important point missing from the article that I've been
| considering since around 2020 when the LLM-for-chip-design trend
| began. A significant divide exists between the capabilities of
| commercial companies and academia/individuals in leveraging LLMs
| for hardware design. For example, Nvidia released ChipNeMo, an
| LLM trained on their internal data, including HDL, tool scripts,
| and issue/project/QA tracking. This gives Nvidia a considerable
| advantage over smaller models trained in academia, which have
| much more limited data in terms of quantity, quality, and
| diversity. It's frustrating to see companies like Nvidia
| presenting their LLM research at academic conferences without
| contributing back meaningful technology or data to the community.
| While I understand they can't share customer data and must
| protect their business interests, these closed research efforts
| and closed collaborations they have with academic groups hinder
| broader progress and open research. This trend isn't unique to
| Nvidia; other companies follow similar practices.
|
| On a more optimistic note, there are now strong efforts within
| the academic community to tackle these problems independently.
| These efforts include creating high-quality, diverse hardware
| design datasets for various LLM tasks and training models to
| perform better on a wider range of HLS-related tasks. As
| mentioned in the article, there is also exciting work connecting
| LLMs with the tools themselves, such as using tool feedback to
| correct design errors and moving towards even more complex and
| innovative workflows. These include in-the-loop verification,
| hierarchical generation, and ML-based performance estimation to
| enable rapid iteration on designs and debugging with a human in
| the loop. This is one area I'm actively working on, both at the
| HDL and HLS levels, so I admit my bias toward this direction.
|
| For more references on the latest research in this area, check
| out the proceedings from the LLM-Aided Design Workshop (now
| evolving into a conference, ICLAD: https://iclad.ai/), as well as
| the MLCAD conference (https://mlcad.org/symposium/2024/).
| Established EDA conferences like DAC and ICCAD have also included
| sessions and tracks on these topics in recent years. All of this
| falls within the broader scope of generative AI, which remains a
| smaller subset of the larger ML4EDA and deep learning for chip
| design community. However, LLM-aided design research is beginning
| to break out into its own distinct field, covering a wider range
| of topics such as LLM-aided design for manufacturing, quantum
| computing, and biology--areas that the ICLAD conference aims to
| expand on in future years.
| SoftTalker wrote:
| If cryptocurrency mining could be significantly optimized (one of
| the example goals in the article) wouldn't that just destroy the
| value of said currency?
| Mistletoe wrote:
| No they all have escalating difficulty algorithms.
|
| https://en.bitcoin.it/wiki/Difficulty
| lqueenan wrote:
| When I think of AI in chip design, optimizations like these come
| to mind,
|
| https://optics.ansys.com/hc/en-us/articles/360042305274-Inve...
|
| https://optics.ansys.com/hc/en-us/articles/33690448941587-In...
| sitkack wrote:
| Had to nop out at "just next token prediction". This article
| isn't worth your time.
| qwertox wrote:
| > If Gary Tan and YC believe that LLMs will be able to design
| chips 100x better than humans currently can, they're
| significantly underestimating the difficulty of chip design, and
| the expertise of chip designers. While LLMs are capable of
| writing functional Verilog sometimes, their performance is still
| subhuman. [...] LLMs primarily pump out mediocre Verilog code.
|
| What is the quality of Verilog code output by humans? Is it good
| enough so that a complex AI chip can be created? Or does the
| human need to use tools in order to generate this code?
|
| I've got the feeling that LLMs will be capable of doing
| everything a human can do, in terms of thinking. There shouldn't
| be an expectation that an LLM is able to do everything, which in
| this context would be thinking about the chip and creating the
| final files in a single pass and without external help. And with
| external help I don't mean us humans, but tools which are
| specialized and also generate some additional data (like
| embeddings) which the LLM (or another LLM) can use in the next
| pass to evaluate the design. And if we humans have spent enough
| time in creating these additional tools, there will come a time
| when LLMs will also be able to create improved versions of them.
|
| I mean, when I once randomly checked the content of a file in The
| Pile, I found an Craigslist "ad" for an escort offering her
| services. No chip-generating AI does need to have this in its
| parameters in order to do its job. So there is a lot of room for
| improvement and this improvement will come over time. Such an LLM
| doesn't need to know that much about humans.
| raydiak wrote:
| The way I read that, I think they're saying hardware acceleration
| of specific algorithms can be 100 times faster and more efficient
| than the same algorithm in software on a general purpose
| processor, and since automated chip design has proven to be a
| difficult problem space, maybe we should try applying AI there so
| we can have a lower bar to specialized hardware accelerators for
| various tasks.
|
| I do not think they mean to say that an AI would be 100 times
| better at designing chips than a human, I assume this is the
| engineering tradeoff they refer to. Though I wouldn't fault
| anyone for being confused, as the wording is painfully awkward
| and salesy.
| erikpukinskis wrote:
| That's my read too, if I'm being generous.
|
| I also think OP is missing the point saying the target
| applications are too small of a market to be worth pursuing.
|
| They're too small to pursue any single one as the market cap
| for a company, but presumably the fictional AI chip startup
| could pursue many of these smaller markets at once. It would be
| a long tail play, wouldn't it?
| Havoc wrote:
| The whole concept of "request for startup" is entirely misguided
| imo.
|
| YC did well because they were good at picking ideas, not
| generating them.
| sigh_again wrote:
| YC doesn't care whether it "makes sense" to use an LLM to design
| chips. They're as technically incompetent as any other VC, and
| their only interest is to pump out dogshit startups in the hopes
| it gets acquired. Gary Tan doesn't care about "making better
| chips": he cares about finding a sucker to buy out a shitty,
| hype-based company for a few billion. An old school investment
| bank would be perfect.
|
| YC is technically incompetent and isn't about making the world
| better. Every single one of their words is a lie and hides the
| real intent: make money.
| jamiequint wrote:
| First, VCs don't get paid when "dogshit startups" get acquired,
| they get paid when they have true outlier successes. It's the
| only way to reliably make money in the VC business.
|
| Second, want to give any examples of "shitty, hype-based
| compan[ies]" (I assume you mean companies with no real revenue
| traction) getting bought out for "a few billion".
|
| Third, investment banks facilitate sales of assets, they don't
| buy them themselves.
|
| Maybe sit out the conversation if you don't even know the
| basics of how VC, startups, or banking work?
| mgraczyk wrote:
| I worked on the Qualcomm DSP architecture team for a year, so I
| have a little experience with this area but not a ton.
|
| The author here is missing a few important things about chip
| design. Most of the time spent and work done is not writing high
| performance Verilog. Designers spent a huge amount of time
| answering questions, writing documentation, copying around boiler
| plate, reading obscure manuals and diagrams, etc. LLMs can
| already help with all of those things.
|
| I believe that LLMs in their current state could help design
| teams move at least twice as fast, and better tools could
| probably change that number to 4x or 10x even with no improvement
| in the intelligence of models. Most of the benefit would come
| from allowing designers to run more experiments and try more
| things, to get feedback on design choices faster, to spend less
| time documenting and communicating, and spend less time reading
| poorly written documentation.
| zachbee wrote:
| Author here -- I don't disagree! I actually noted this in the
| article:
|
| > Well, it turns out that LLMs are also pretty valuable when it
| comes to chips for lucrative markets -- but they won't be doing
| most of the design work. LLM copilots for Verilog are, at best,
| mediocre. But leveraging an LLM to write small snippets of
| simple code can still save engineers time, and ultimately save
| their employers money.
|
| I think designers getting 2x faster is probably optimistic, but
| I also could be wrong about that! Most of my chip design
| experience has been at smaller companies, with good
| documentation, where I've been focused on datapath architecture
| & design, so maybe I'm underestimating how much boilerplate the
| average engineer deals with.
|
| Regardless, I don't think LLMs will be designing high-
| performance datapath or networking Verilog anytime soon.
| mgraczyk wrote:
| Thanks for the reply!
|
| At large companies with many designers, a lot of time is
| spent coordinating and planning. LLMs can already help with
| that.
|
| As far as design/copilot goes, I think there are reasons to
| be much more optimistic. Existing models haven't seen much
| Verilog. With better training data it's reasonable to expect
| that they will improve to perform at least as well on Verilog
| as they do on python. But even if there is a 10% chance it's
| reasonable for VCs to invest in these companies.
| catlifeonmars wrote:
| I'm actually curious if there even is a large enough corpus
| of Verilog out there. I have noticed that even tools like
| Copilot tend to perform poorly when working with DSLs that
| are majority open source code (on GitHub no less!) where
| the practical application is niche. To put this in other
| terms, Copilot appears to _specialize_ on languages,
| libraries and design patterns that have wide adoption, but
| does not appear to be able to _generalize_ well to
| previously unseen or rarely seen languages, libraries, or
| design patterns.
|
| Anyway that's largely anecdata/sample size of 1, and it
| could very well be a case of me holding the tool wrong, but
| that's what I observed.
| benchmarkist wrote:
| This is a great article but the main principle at YC is to assume
| that technology will continue progressing at an exponential rate
| and then thinking about what it would enable. Their proposals are
| always assuming the startups will ride some kind of Moore's Law
| for AI and hardware synthesis is an obvious use case. So the
| assumption is that in 2 years there will be a successful AI
| hardware synthesis company and all they're trying to do is get
| ahead of the curve.
|
| I agree they're probably wrong but this article doesn't actually
| explain why they're wrong to bet on exponential progress in AI
| capabilities.
| aubanel wrote:
| I know nothing about chip design. But saying "Applying AI to
| field X won't work, because X is complex, and LLMs currently have
| subhuman performance at this" always sounds dubious.
|
| VCs are not investing in the current LLM-based systems to improve
| X, they're investing in a future where LLM based systems will be
| 100x more performant.
|
| Writing is complex, LLMs once had subhuman performance, and yet.
| Digital art. Music (see suno.AI) There is a pattern here.
| zachbee wrote:
| I didn't get into this in the article, but one of the major
| challenges with achieving superhuman performance on Verilog is
| the lack of high-quality training data. Most professional-
| quality Verilog is closed source, so LLMs are generally much
| worse at writing Verilog than, say, Python. And even still,
| LLMs are pretty bad at Python!
| theptip wrote:
| That's what your VC investment would be buying; the model of
| "pay experts to create a private training set for fine
| tuning" is an obvious new business model that is probably
| under-appreciated.
|
| If that's the biggest gap, then YC is correct that it's a
| good area for a startup to tackle.
| e_y_ wrote:
| That's probably where there's a big advantage to being a
| company like Nvidia, which has both the proprietary chip
| design knowledge/data and the resources/money and AI/LLM
| expertise to work on something specialized like this.
| DannyBee wrote:
| I strongly doubt this - they don't have enough training
| data either - you are confusing (i think) the scale of
| their success with the amount of verilog they possess.
|
| IE I think you are wildly underestimating both the scale of
| training data needing, and wildly overestimating the amount
| of verilog code possessed by nvidia.
|
| GPU's work by having moderate complexity cores (in the
| scheme of things) that are replicated 8000 times or
| whatever. That does not require having 8000 times as much
| useful verilog, of course.
|
| The folks who have 8000 different chips, or 100 chips that
| each do 1000 things, would probably have orders of
| magnitude more verilog to use for training
| jrflowers wrote:
| I like this reasoning. It is shortsighted to say that LLMs
| _aren't_ well-suited to something (because we cannot tell the
| future) but it is not shortsighted to say that LLMs _are_ well-
| suited to something (because we cannot tell the future)
| cruffle_duffle wrote:
| I kinda suspect that things that are expressed better with
| symbols and connections than with text will always be a poor
| fit to large LANGUAGE models. Turning what is basically a
| graph into a linear steam of text descriptions to tokenize
| and jam into an LLM has to be an incredibly inefficient and
| not very performant way of letting "AI" do magic on your
| circuits.
|
| Ever try to get ChatGPT to play scrabble? Ever try to
| describe the board to it and then all the letters available
| to you? Even its fancy pants o1 preview performs absolutely
| horrible. Either my prompting completely sucks or an LLM is
| just the wrong tool for the job.
|
| It's great for asking you to score something you just created
| provided you tell it what bonuses apply to which words and
| letters. But it has absolutely no concept of the board at
| all. You cannot use to optimize your next move based on the
| board and the letters.
|
| ... I mean you might if you were extremely verbose about
| every letter on the board and every available place to put
| your tiles, perhaps avoiding coordinates and instead
| describing each word, its neighbors and relationships to
| bonus squares. But that just highlights how bad a tool an LLM
| is for scrabble.
|
| Anyway, I'm sure schematics are very similar. Maybe somebody
| we will invent good machine learning models for such things
| but an LLM isn't it.
| kuhewa wrote:
| > Writing is complex, LLMs once had subhuman performance,
|
| And now they can easily replace mediocre human performance, and
| since they are tuned to provide answers that appeal to humans
| that is especially true for these subjective value use cases.
| Chip design doesn't seem very similar. Seems like a case where
| specifically trained tools would be of assistance. For some
| things, as much as generalist LLMs have surprised at skill in
| specific tasks, it is very hard to see how training on a broad
| corpus of text could outperform specific tools -- for first
| paragraph do you really think it is not dubious to think a
| model trained on text would outperform Stockfish at chess?
| duped wrote:
| AI still has subhuman performance for art. It feels like the
| venn diagram of people who are bullish on LLMs and people who
| don't understand logistic curves is a circle.
| afro88 wrote:
| They (YC) are interested in the use of LLMs to make the process
| of designing chips more efficient. Nowhere do they talk about
| LLMs actually designing chips.
|
| I don't know anything about chip design, but like any area in
| tech I'm certain there are cumbersome and largely repetitive
| tasks that can't easily be done by algorithms but can be done
| with human oversight by LLMs. There's efficiency to be gained
| here if the designer and operator of the LLM system know what
| they're doing.
| aabhay wrote:
| Except that's now a very standard pitch for technology across
| basically any industry, and cheapens the whole idea of YC
| presenting a grand challenge.
| jaxr wrote:
| I don't know the space well enough, but I think the missing piece
| is that YC 's investment horizon is typically 10+ years. Not only
| LLMs could get massively better, but the chip industry could be
| massively disrupted with the right incentives. My guess is that
| that is YC's thesis behind the ask.
| frizdny5 wrote:
| The bottleneck for LLM is fast and large memory, not compute
| power.
|
| Whoever is recommending investing in better chip(ALU) design
| hasn't done even a basic analysis of the problem.
|
| Tokens per second = memory bandwidth divided by model size.
| zachbee wrote:
| hi, this is my article! thanks so much for the views, upvotes,
| and comments! :)
| zhyder wrote:
| As a former chip designer (been 16 years, but looks like tools
| and our arguments about them haven't changed much), I'm both more
| and less optimistic than OP:
|
| 1. More because fine-tuning with enough good Verilog as data
| should let the LLMs do better at avoiding mediocre Verilog
| (existing chip companies have more of this data already though).
| Plus non-LLM tools will remain, so you can chain those tools to
| test that the LLM hasn't produced Verilog that synthesizes to a
| large area, etc
|
| 2. Less because when creating more chips for more markets (if
| that's the interpretation of YC's RFS), the limiting factor will
| become the cost of using a fab (mask sets cost millions), and
| then integrating onto a board/system the customer will actually
| use. A half-solution would be if FPGAs embedded in CPUs/GPUs/SiPs
| on our existing devices took off
| theptip wrote:
| I disagree with most of the reasoning here, and think this post
| misunderstands the opportunity and economic reasoning at play
| here.
|
| > If Gary Tan and YC believe that LLMs will be able to design
| chips 100x better than humans currently can, they're
| significantly underestimating the difficulty of chip design, and
| the expertise of chip designers.
|
| This is very obviously not the intent of the passage the author
| quotes. They are clearly talking about the speedup that can be
| gained from ASICs for a specific workload, eg dedicated mining
| chips.
|
| > High-level synthesis, or HLS, was born in 1998, when Forte
| Design Systems was founded
|
| This sort of historical argument is akin to arguing "AI was bad
| in the 90s, look at Eliza". So what? LLMs are orders of magnitude
| more capable now.
|
| > Ultimately, while HLS makes designers more productive, it
| reduces the performance of the designs they make. And if you're
| designing high-value chips in a crowded market, like AI
| accelerators, performance is one of the major metrics you're
| expected to compete on.
|
| This is the crux of the author's misunderstanding.
|
| Here is the basic economics explanation: creating an ASIC for a
| specific use is normally cost-prohibitive because the cost of the
| inputs (chip design) is much higher than the outputs (performance
| gains) are worth.
|
| If you can make ASIC design cheaper on the margin, and even if
| the designs are inferior to what an expert human could create,
| then you can unlock a lot of value. Think of all the places an
| ASIC could add value if the design was 10x or 100x cheaper, even
| if the perf gains were reduced from 100x to 10x.
|
| The analogous argument is "LLMs make it easier for non-
| programmers to author web apps. The code quality is clearly worse
| than what a software engineer would produce but the benefits
| massively outweigh, as many domain experts can now author their
| own web apps where it wouldn't be cost-effective to hire a
| software engineer."
| aabhay wrote:
| I think the problem with this particular challenge is that it is
| incredibly non-disruptive to the status quo. There are already
| 100s of billions flowing into using LLMs as well as GPUs for chip
| design. Nvidia has of course laid the ground work with its
| culitho efforts. This kind of research area is very hot in the
| research world as well. It's by no means difficult to pitch to a
| VC. So why should YC back it? I'd love to see YC identifying
| areas where VC dollars are not flowing. Unfortunately, the other
| challenges are mostly the same -- govtech, civictech, defense
| tech. These are all areas where VC dollars are now happily
| flowing since companies like Anduril made it plausible.
| gyudin wrote:
| Thank god humans are superior in chips design especially when you
| have dozens billions of dollars behind you, just like Intel. Oh
| wait.
| igorkraw wrote:
| I did my PhD on trying to use ML for EDA (de novo design/topology
| generation, because deepmind was doing placement and I was not
| gonna compete with them as a single EE grad who self taught
| ML/optimization theory during the PhD).
|
| In my opinion, part of the problem i that training data is scarce
| (real world designs are literally called "IP" in the industry
| after all...), but more than that, circuit design is basically
| program synthesis, which means it's _hard_. Even if you try to be
| clever, dealing with graphs and designing discrete objects
| involves many APX-hard/APX-complete problems, which is _FUN_ on
| the one had, but also means it's tricky to just scale through, if
| the object you are trying to do is a design that can cost
| millions if there's a bug...
| binbag wrote:
| Software folk underestimating hardware? Surely not.
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