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I wrote yesterday:
"A lady claims via LinkedIn that an AI service produced a bad Verilog=20
code, so
she concluded that an AI is not going to threaten her job, and I wrote to h=
er
that she deserves a refund."
Dear all:
"User Agreement
Effective on November 20, 2024
[. . .]
8.2. Don=E2=80=99ts
You agree that you will not:
[. . .]
4. Copy, use, display or distribute any information (including content)=20 obtained from the Services, whether directly or through third parties=20
(such as search tools or data aggregators or brokers), without the=20
consent of the content owner (such as LinkedIn for content it owns);"
says
HTTPS://WWW.LinkedIn.com/legal/user-agreement#dos
I asked Ms. Sharada Yeluri for permission to republish from that LinkedIn=
=20
thread. She likes this question, so I republish . . .
"Sharada Yeluri
=E2=80=A2 3:e+Premium =E2=80=A2 3:e+
Engineering Leader
6 m=C3=A5n [months ago i.e. circa February 2025] =E2=80=A2 Redigerad [Swedi=
sh for=20
edited] =E2=80=A2 6 m=C3=A5nader sedan [months ago] =E2=80=A2 Redigerad = =E2=80=A2 Synligt f=C3=B6r alla, p=C3=A5=20
och utanf=C3=B6r LinkedIn
F=C3=B6lj [Follow]
ChatGPT o1 with advanced reasoning=E2=80=A6 excels at competition-level mat= h,=20
solves PhD-level science questions, tackles complex multi-step problems=20
with chain-of-thought reasoning=E2=80=A6 The list goes on.
Curious about its prowess, I decided to test its ability to develop=20
Verilog RTL code for a functional block that=E2=80=99s commonly found in mo= st=20
networking and XPU ASICs: a buffer manager. After all, they charge $200=20
per month, so there must be some magic.
Grudgingly, I paid the fee and posed a challenge: Build a buffer manager=20
for a 16K entry-deep buffer that is 128 bits wide, shared dynamically=20 between 256 queues. The module should sustain one enqueue and one dequeue=
=20
every cycle without stalls... Use SRAMs for linked list structures, and=20
yes, the SRAMs have two-cycle read latencies...
I know there aren=E2=80=99t many open-source Verilog designs for hashtag#Ch= atGPT=20
to learn from. Still, with its "advanced" reasoning abilities, I expected=
=20
a decent output.
It churned out an RTL module and a Verilog test bench=E2=80=94points for ef= fort.=20
When I pointed out how the design could not handle back-to-back dequeues=20 from the same queue, it gave up too quickly and declared there was no way=
=20
to design it without stalling the inputs. I nudged it towards approaches=20 like doubly linked lists or ping-pong buffers. It understood the concepts=
=20
and even explained them back to me, like a student trying to impress a=20 professor... =F0=9F=98=8A
When the RTL didn=E2=80=99t give the correct results, I directly fed back t= he=20
simulation results from its test bench for it to analyze. After a few=20 feedback iterations, the enqueues started working=E2=80=94progress!
The dequeues, however, remained stubbornly broken. Hoping to simplify=20 things, I relaxed the constraints, allowing a 5-cycle gap. No luck...=20 Instead, ChatGPT decided the simulator was wrong=E2=80=94an audacious claim=
for=20
an AI model still learning to count pipeline stages.
Eventually, I debugged the RTL myself and found the culprit - a typo.=20
After fixing it, the dequeues worked. However, the design still lacked=20 hazard checks for back-to-back dequeues, and after an hour of trying to=20 teach pipeline bypasses, I called it quits.
The good news? =F0=9F=A4=94
While the ChatGPTs and copilots might take over sw engineer jobs, they=20
are far from snatching jobs from ASIC engineers=E2=80=A6 =F0=9F=98=8A
They may argue about the lack of open-source Verilog for AI models to=20
train on - chip designs are locked away tighter than bank vaults. But if=20 ChatGPT can solve Olympiad math through reasoning, why does reasoning=20 through pipeline hazards feel like rocket science to it? =F0=9F=A4=94
The pace of innovation needed to achieve hashtag#AGI is directly tied to=20 advancements in XPUs and the networking hardware they rely on. If AI=20 companies are serious about accelerating AGI development, we need models=20 that can reason through complex chip design problems and help compress=20 design cycles. After all, these chips are the foundation for their AGI=20 dreams.
hashtag#OpenAI team, now that the Olympiad math is behind you, how about=20
the chip design challenge next?"
"Sharada Yeluri
F=C3=B6rfattare [LinkedIn Original Poster]
Engineering Leader
6 m=C3=A5n
Andreas Olofsson, 100% agree. But again, the idea behind reasoning models=
=20
is that they work well even in the absence of tons of data during=20
training. The model seems to understand all the Verilog syntax and can=20
spit out hundreds of lines of code that compiles well. when I explain=20 pipelining concepts, it understands and repeats back it's interpretation=20 with examples. It almost felt like I was talking to a new college grad.=20
But, it fell short of actually implementing the concepts back in Verilog.=
=20
It probably needs fine-tuning during the training phase with examples=20
where the feedback from the simulation can be used to train the models.=20
Just thinking out loud."
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
6 m=C3=A5n
Gaurav Vaid , hmm.. interesting thoughts."
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
6 m=C3=A5n
Rob Sprinkle, I haven't used Haskell personally. So, I won't be able to=20 comment on it. I think the quality of the code improved a lot from the=20
first pass to when I finally jumped in. It actually does learn when you=20 teach new concepts. For example, when I told her that the pipeline names=20 were all messed up and it should use strict suffixes like _p0, _p1, etc.=20
to distinguish between the pipeline stage signals, it rewrote the code so=
=20
well that it eventually made it easy for me to debug. If we have to=20 intervene from the beginning, it defeats the purpose IMO.."
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
6 m=C3=A5n
Ivan Djordjevic , reasoning models, as claimed by openAI, are supposed to=
=20
be more intelligent than parrots :)"
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
6 m=C3=A5n
From=20Open AI: " The models use a sophisticated chain-of-thought reasoning= =20
process, allowing them to break down intricate problems into manageable=20 steps." My experiment aims to see if the model can solve the problem on=20
its own. Even then, I broke it down step by step, simplified the problem=20 several times, asked it to reset and start over, etc., but I just could=20
not get it to solve pipeline hazards. If you have better luck, do let me=20 know."
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
6 m=C3=A5n
Varun Uniyal I don=E2=80=99t think chipNemo can solve this. But I could be = wrong.=20
Try ir out=E2=80=A6"
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
6 m=C3=A5n
Rajesh Parikh, Great thoughts. Maybe, in addition to more data during=20 training, these domain-specific models also need access to verilog=20 simulators and test benches written by either humans or other models=20
during training, as well as inference."
"Paul Colin Gloster
=E2=80=A2 Du [Thou in Swedish - i.e. I]
Researcher at Universidade de Coimbra
6 m=C3=A5n
Dear Sharada Yeluri: Happy New Year! Demand a refund!" She finds this=20 comment to be funny. I seriously mean it.
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
(redigerat)
6 m=C3=A5n
Debajyoti Pal, I understand your concerns. But think about it this way:=20 around 35-40 years ago, people used to hand-draw the schematics of logic=20 gates for their chips. There was no concept of using an EDA tool to=20 synthesize the gates. When the EDA tools came out, a lot of design=20
engineers protested that the tools did not know how to come up with an=20 area-efficient netlist that also meets timing and argued that we should=20 still rely on hand-drawn logic gates for high-speed datapath. I remember=20
at Sun Microsystems, we used to have special teams to do datapath design=20 where the logic to gates was done manually, and engineers did manual P&R.=
=20
Gradually, as the tools became better at what they do, we started=20
trusting them for all digital logic design. The reason the tools got more=
=20
advanced is that EDA vendors built feedback systems where the timing is=20
fed back to make the synthesis and P&R better. LEC and formal methods=20 ensured that the netlist is functionally equivalent to RTL, etc. I see=20
the same transition that will eventually happen again, with tools=20
generating RTL from high-level specs using advanced reasoning and humans=20 using other verifier tools to ensure the generated RTL can be used. It is=
=20
a matter of when not if. IMO"
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
(redigerat)
6 m=C3=A5n
Dawei Wang, Nice to hear from you. I did what you suggested to some=20
extent. The Verilog-based TB and RTL are both generated by ChatGPT, and I=
=20
was feeding back the result from the TB as is to ChatGPT so that it can=20 fine-tune the RTL until it works."
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
6 m=C3=A5n
Raja Ramkaran Reddy Rudravaram, Thank you. Yes, I will try the RAG one=20 next."
"Srinivas Lingam, thanks. I agree that model developers haven't yet=20
focused on enhancing the model's capabilities for chip design challenges.=
=20
Most are probably giving up too soon with the excuse that they can't find=
=20
enough data. Even with limited data, can the model developers use the=20
same RL techniques they have used for math to improve the models for RTL=20 coding? Could they use Verilog simulators as verifiers during=20
post-training fine-tuning? This, combined with agentic workflows (where=20
the generated RTL is continuously checked with simulation by the agents=20
and fed back to the model until it converges), could probably yield good=20 results. I hope to see more innovation on this front."
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
6 m=C3=A5n
Saurabh Chakraborty , did you try this challenge?"
"Paul Colin Gloster
=E2=80=A2 Du
Researcher at Universidade de Coimbra
1 sek
Dear Mister Patrick Lehmann: Demand a refund! Limited LinkedIn does not=20 allow having more than one reaction icon set. I set that comment by you=20
to insightful, and I also want to set it to funny!"
"Sharada Yeluri
F=C3=B6rfattare
Engineering Leader
6 m=C3=A5n
Sreenivas Nandam It is not a syntax error. It has one extra pipeline for=20
the valid bur, not for the address, so it could never correctly line up=20
the read data with the requester. For some reason, it was not able to=20
figure that out."
--708268602-2091128679-1754908162=:530417--
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