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.
> “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????)
My experience with being an adult, in general, is that many people who went to university don't believe that any given course taught them anything meaningful.
I can absolutely believe that such people didn't learn and remember anything meaningful from those courses. Whether the course is to blame, is far more questionable.
It's the same as all the people who say "Why didn't high school teach me how to balance a check book or calculate a mortgage or blah blah?"
In nearly every case, they literally did, but you weren't paying attention.
You also had to cheat off me to pass biology, so I'm going to go ahead and press X to doubt that you "understand the immune system"
We are surrounded by people who failed to invest in their own education, and instead of facing that awful reality, they INSIST that WE are the dumb ones.
It's infuriating.
Why should we expect a general-purpose instruction-tuned LLM to get this right in the first place? I am not at all surprised it didn't work, and I would be more than a little surprised if it did.
The argument goes: Language encodes knowledge, so from the vast reams of training data, the model will have encoded the fundamentals of electromagnetism. This is based in the belief that LLMs being adept at manipulating language, are therefore inchoate general intelligences, and indeed, attaining AGI is a matter of scaling parameters and/or training data on the existing LLM foundations.
RC circuits man.
…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!”
Does an LLM know math? Not like we do. There’s no deductive logic in there; it’s all statistical inferences from language. An LLM doesn’t “work through” a circuit diagram systematically the way a physics student would. It observes the entire diagram at once, and then guesses the most likely next token.
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.
Hello, fellow tech enthusiasts, just stopping by to announce I performatively can't tell the difference between "Latest big tech product (TM)" and Homo Sapiens Sapiens!!!
I'll be seeing you in the next LLM related message thread with the same exact comment!!! As you were!!!
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.
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?
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.
ESM3: https://www.evolutionaryscale.ai/blog/esm3-release
AlphaProof/AlphaGeometry2: https://deepmind.google/discover/blog/ai-solves-imo-problems...
MatPilot discovering new materials: https://arxiv.org/abs/2411.08063
Then of course NVidia Omniverse with their digital-twin learning.
https://blog.google/technology/ai/google-ai-big-scientific-b...
But I certainly agree in general. It’s been years and there are still no independent novel discoveries afaik.
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.
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?
https://www.reuters.com/article/business/peloton-raises-12-b...
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.
> 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.
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.
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.
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.
How many experts do YC have on chip design?
A bit level (non von Neumann) general purpose systolic array could greatly speed up AI computations, along with almost everything else. It's a chip to do general purpose computation.
The chip design is almost trivial. I'd expect someone with a few years of experience could knock it out in a few days. I hope to field a design in the next TinyTapeout (I'm on a fixed income, so I've had to wait a while)
The real problem is programming. We're talking vast greenfields that go on forever. There's no good way to target the architecture, you certainly wouldn't want to use Verilog or any other HDL.
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.
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.
If that’s the biggest gap, then YC is correct that it’s a good area for a startup to tackle.
If LLMs will do well in the space for some use case it's the established chip designers that will benefit from it, not a small startup.
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?
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.
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.
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?
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.
We have a bunch of AI initiatives in my company but most of them are about using Copilot to help write scripts to automate the design flow. Our physical design flow are thousands of lines of Tcl and Python code.
The article mentions High Level Synthesis. I've been reading about this since my first job in the 1990's. I've worked on at least 80 chips and I've never seen any chip use one of these tools except for some tiny section that was written by some academics who didn't want to learn Verilog for reasons.
It's fundamentally important when doing hardware design to work in a language that _expresses_ itself like you're designing hardware. Verilog (for all its faults) shines there because it feels like you're writing a slightly higher level netlist. That's not the case with SC and friends, which doesn't allow you to think in hardware. Languages like BSV and SV are functionally similar but they force you to think in similar ways to Verilog, meaning you can write much tighter high-level code.
I'd be interested in your experience, but I feel that using normal programming languages to build hardware is an abstraction failure. Which is why it performs so poorly.
Anyways, their IP very clearly violated the standards of a very well known interface, which could have spelled disaster at tape-out. I had to fight tooth-and-nail, and spent lots of my company's time trying to convince this third-party vendor that this was an actual issue. Only months later were they convinced. The revised code kept coming back and failing interface checks, which shows that they weren't doing these checks on their end. All I could think is, "this can't go well..."
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.
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.
> 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.
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.
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
> 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.
I may be confused, but isn’t the author fundamentally misunderstanding YC’s point? I read YC as simply pointing out the benefit of specialized compute, like GPUs, not making any point about the magnitude of improvement LLMs could achieve over humans.
From my reading of the RFS (not the video) it appears they are essentially asking for the next Groq or SambaNova.
Personally, this kind of communication issue would give me a long pause if I was considering YC for this segment, as this is a fairly basic thesis to communicate, and if a basic thesis can be muddled, can the advice provided be strong as well, especially compared to peer early stage funders in this space?
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.
If the evaluation of the approach is "it works great if you train it on a few decades of the best designs from a successful fabless semiconductor company", I would say that if you plan to use that method as a startup, you're clearly going to fail. Nobody's going to give away their crown jewels to train an LLM that designs chips for other companies.
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
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.
If you’re creative enough to figure out different tools for humans, you are creative enough to figure out different tools for LLMs
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.
We at Silogy [0] are directly targeting the problem of verification productivity using AI agents for test debugging. We analyze code (RTL, testbench, specs, etc.) along with logs and waveforms, and incorporate interactive feedback from the engineer as needed to refine the hypothesis.
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
Gary Tan's was right[1] in that there is a fundamental inefficiency inherent in the von Neumann architecture we're all using. This gross impedance mismatch[4] is a great opportunity for innovation.
Once ENIAC was "improved" from its original structure to a general purpose compute device in the von Neumann style, it suffered a 83% loss in performance[2] Everything since is 80 years of premature optimization that we need to unwind. It's the ultimate pile of technical debt.
Instead of throwing maximum effort into making specific workloads faster, why not build a chip that can make all workloads faster instead, and let economy of scale work for everyone?
I propose (and have for a while[3]) a general purpose solution.
A systolic array of simple 4 bits in, 4 bits out, Look Up Tables (LUTs) latched so that timing issues are eliminated, could greatly accelerate computation, in a far nearer timeframe.
The challenges are that it's a greenfield environment, with no compilers (though it's probable that LLVM could target it), and a bus number of 1.
[1] https://www.ycombinator.com/rfs-build#llms-for-chip-design
[2] https://en.wikipedia.org/wiki/ENIAC#Improvements
[3] https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...
For example, how it would implement a 1-bit full adder? Like the nitty-gritty details: which input on which cell represents input A, which represents input B, and which represents carry-in? Which output is sum and which is carry-out? What are the functions programmed into each node that it uses?
Then show how to build a 2-bit adder from there.
I don't think he's arguing that. More that ASICs can be 100x better than CPUs for say crypto mining and that using LLM type stuff it may be possible to make them for other applications where there is less money available to hire engineers.
(the YC request https://www.ycombinator.com/rfs-build#llms-for-chip-design)
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.
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.
Replace all asserts with expected ==expected and most people won't notice.
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.
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.
To be a bit acerbic, and inspired by Arthur C. Clarke, I might say: "Any sufficiently complex business could be indistinguishable from Theranos".
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.
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.
I have been working on FPGA's and, in general, programmable logic, for somewhere around thirty years (started with Intel programmable logic chips like the 5C090 [0] for real time video processing circuits.
I completely skipped over the whole High Level Synthesis (HLS) era that tried to use C, etc. for FPGA design. I stuck with Verilog and developed custom tools to speed-up my work. My logic was simple: If you try to pound a square peg into a round hole, you might get it done yet, the result will be a mess.
FPGA development is hardware development. Not software. If you cannot design digital circuits to begin with, no amount of help from a C-to-Verilog tool is going to get you the kind of performance (both in terms of time and resources) that a hardware designer can squeeze out of the chip.
This is not very different from using a language like Python vs. C or C++ to write software. Python "democratizes" software development at a cost of 70x slower performance and 70x greater energy consumption. Sure, there are places where Python makes sense. I'll admit that much.
Going back to FPGA circuit design, the issue likely has to do with the type, content and approach to training. Once again, the output isn't software; the end product isn't software.
I have been looking into applying my experience in FPGA's across the entire modern AI landscape. I have a number of ideas, none well-formed enough to even begin to consider launching a startup in the sector. Before I do that I need to run through lots of experiments to understand how to approach it.
[0] https://www.cpu-galaxy.at/cpu/ram%20rom%20eprom/other_intel_...
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?
Current LLMs can’t do it, but the assumption that that’s what YC meant seems wildly premature.
The purpose of capital is to make progress from where we are now.
https://optics.ansys.com/hc/en-us/articles/360042305274-Inve...
https://optics.ansys.com/hc/en-us/articles/33690448941587-In...
YC did well because they were good at picking ideas, not generating them.
This doesn't line up with the perennial attitude (as discussed by pg) that YC picks people/teams and not ideas, because while ideas and approaches may change, the people are the same and having a good founder, co-founder and team matters the most.
Their M.O. is to avoid getting too attached to an idea because, in the process of actually building the company, pivots may be required. And so the focus is on a team moreso than a business plan, which again, is not something pg is particularly fond of seeing especially the ones that have lengthy (and therefore improbable/unrealistic) forecasts.
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...
But selling shovels that are useful in many small markets can still be a viable play, and that’s how I understand YC’s position here.
Language is cool and immensely useful. LLMs, however, are fundamentally flawed from their basic assumptions about how language works. The distribution hypothesis is good for paraphrasing and summarization, but pretty atrocious for real reasoning. The concept of an idea living in a semantic "space" is incompatible with simple vector spaces, and we are starting to see this actually matter in minutia with scaling laws coming into play. Chip design is a great example of where we cannot rely on language alone to solve all our problems.
I hope to be proven wrong, but still not sold on AGI being within reach. We'll probably need some pretty significant advancements in large quantitative models, multi-modal models and smaller, composable models of all types before we see AGI
With respect to AGI in its broadest sense: indeed it is not in reach. I think that is for the better!
Other intelligent effects are coincidental.
Other intelligent effects are coincidental.
> 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.”
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.
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.
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.
I mean I assume the best is heavily guarded.
the AI hype train is basically investors not understanding tech, don’t get me wrong AI in itself could be a huge thing if used right but the things getting the most attention in the current market aren’t it
Would appreciate the collective energy being spent instead towards adding to amor refining Garry’s request.
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.
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.
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.
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.
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.
