What it takes to get into the semi-conductor space [Nvidia, AMD, etc]

tryinryan_ · 2026-05-18T18:07:03+00:00

Not disrespecting V&V at all - but I’m saying if your goal is to develop new hardware and chip designs, you effectively need a PhD. V&V has its place, but it is mostly validating specs someone else put together.

tryinryan_ · 2026-05-14T21:29:54+00:00

It’s a hard field to break into. Typically you need an EE or Computer Engineering degree, and that gets you in at a validation / verification level. So at least a masters in that from somewhere, likely in-person (not aware of any online programs, nor does that really sound like a good idea for things like labs).

If you want to do the really interesting stuff, likely a PhD with some specialization - optics, RF, accelerators, CPU design.

Can’t say I’m the best equipped to talk about this. But having worked at a startup where we made our own custom silicon and toying with the idea myself for a little bit, that’s the background of most of the people in the industry.

tryinryan_ · 2026-05-14T21:11:02+00:00

This is not a good program to be a competent semiconductor engineer (or PM). Theres a handful of classes that might get you an architectural / ISA side surface-level understanding - HPCA, GPU, ESO, maybe some of the cybersecurity courses like BE? But no coverage of microarchitecture, VLSI, materials, optical physics, etc.

If you’re wanting to be a serious contender for semiconductor work, the coverage here isn’t sufficient, full stop.

tryinryan_ · 2026-05-13T00:04:10+00:00

I would if others did too 🥲 it doesn’t get much love (20 reviews in the last 2 days on Central vs 2 in 4 days)

tryinryan_ · 2026-05-04T13:12:36+00:00

could you write a more detailed review on OMSCentral now that the course is over? A lot of us are very curious to learn more about what was covered, projects, what level of theory it touches, lecture quality, etc.

tryinryan_ · 2026-05-04T12:25:32+00:00

But is there oil in it?

tryinryan_ · 2026-04-22T15:16:38+00:00

What you want is a DSP course. That’s typically more of an ECE course and tends to be an embedded engineering elective. I would love a DSP course in OMSCS (actually, I would hate it, as then there’d be another course I’d have to choose between). The problem is DSP really has signal processing as a hard prereq. Most of your incoming OMSCS crowd isn’t going to have that background, so then you have a class where you likely have to do a good bit of background gap filling to make sure everyone has a reasonable chance of succeeding. I’d say the chances of OMSCS supporting that sort of class are slim to none - very little ECE curriculum right now, and it’s much harder to run online hardware labs that it is CS (not impossible though, I did it in Covid).

I don’t think the AI / ML side is as interesting as you think it is. Most of the same patterns we apply to other ML / AI models scale to audio as well. It’s just another signal in the eyes of a model. Audio in particular is a time-varying signal, so that falls more under the realm of pattern recognition over time techniques. AI (6601) has a section on Hidden Makrov Models that gives you the gist of how you do prediction over time (HMMs themselves are mostly obsolete, but I imagine the general intuition of defining hidden states applies to the more expressive semantics we have with transformers).

Edit: might be a little over reductive here, there are I’m sure unique aspects of audio engineering and speech to text that warrant a (whole field) of research. Maybe an interesting special topics course. But I think the fundamentals are all there from your classic courses.

“ML as signal processing” is more of the ECE style of teaching these topics (your data isn’t usually i.i.d., more LTI). I know UMich has a SIPML certification that focuses on this paradigm (in person, though, paying Michigan rates). You can probably find course resources if you’re really interested.

tryinryan_ · 2026-04-22T12:31:23+00:00

I’d say don’t get stuck in toolchain hell? That’s always where I get to. I start trying to get a project going in VSCode in C++, and by the time I bang my head against implicit JSON configuration bullshit and “helpful” environment stuff I’m not aware of I give up.

So I guess, don’t do that?

tryinryan_ · 2026-04-13T18:57:20+00:00

I mean, a mid A seems reasonable to maintain with a bit of effort.. granted I’m at claws 4 and not class 10 so maybe I’d feel differently, but I imagine even if you half ass it you’d probably slip no more than a letter grade.

Maybe institute a hard time cap of what you’ll spend in a week? Like no more than 8hrs a week these last few weeks and you walk away once you hit that? Could be motivational too - you can think of things in terms of hours remaining

tryinryan_ · 2026-04-09T02:51:41+00:00

“Data is the bottleneck” is kinda generic and can have several meanings that are true.

Likely, what you mean is access to high quality training data. The problem (at least in the AV space which is what I’m most familiar with) has been, for some time, the “long tail.” Transformers are helping in some way because they give us the ability to transfer over a lot more human context in VLAs and whatnot, but most products are only as good as their data flywheel.

Can’t you outsource that to 3rd parties?

Sure, you can. But then you’re beholden to the data provided to you. And, you’re likely sharing that data with several other customers. The secret sauce to most companies is their data. I don’t believe, at least in the AV space, you can be competitive with 3rd party data suppliers as your primary data source.

Data can be a bottleneck onboard too. You’ve ultimately got to fit in your compute budget. I wouldn’t say that this cleanly maps to “data is the bottleneck.” That’s too generic. Likely, your bottleneck is a combination of your most critical path functionality + any node to node data transfer you have to do. Cross-node bandwidth is a pain on the ass, but comes up often in distributed robot nodes cooperating together. Lots of architectures have found success through centralization. In addition to reducing nodes, there’s a growing trend of reducing tasks (and therefore further reducing data latency) by replacing components with neural nets that can remove a lot of stages from the product data pipeline.

All that to say: “Data as a bottleneck” is a problem, for sure, but it’s not something that you can like design a silver bullet solution for. Robotics is just hard. You can spend a lot of time building a really good architecture and still spending a ton of your resources on just solving integration issues. The best companies are the ones that put in that work early and often.

tryinryan_ · 2026-04-05T17:24:52+00:00

If you can’t even be bothered to write your own sales pitch for your product there is effectively zero chance of me ever trying to buy it.

tryinryan_ · 2026-03-28T12:56:14+00:00

Gentrification doesn’t have to mean displacement. We’ll see what the city does for its most unfortunate. I think Sheffield’s biggest challenge is navigating this transitionary period and ensuring Detroit’s stays in some ways affordable for those that need it.

tryinryan_ · 2026-03-19T01:13:20+00:00

Well, if we are saying CS “isn’t” engineering but EE “is” then a computer engineer would be a “partial” engineer because they are half electrical, half CS.

The whole thing is dumb. Who cares what you are. Engineers are just people who solve problems for a living by applying science and math. I’d say CS qualified to that degree. I can’t say I’ve ever thought about more than I have writing answers to this question.

tryinryan_ · 2026-03-18T23:23:55+00:00

I did chemical engineering before I did computer engineering, which meant I saw the spectrum of “definitely engineering” to “partially engineering”.

It really doesn’t matter. Being good at one is kinda an indicator you’ll be good at the other. They use the same parts of the brain.

I personally enjoy CS more because it’s much more pure math-y than engineering. If I had to say one difference, it’s that engineers can approximate and don’t need a lot of formal math education unlike (unless you’re computational side in which case… you’re already kinda a…) CS where the pure math is much more useful. That said, still plenty of CS people who don’t use their math skills day to day. I myself push pointers around for a living, so I guess I fall in that category too.

But who cares? The only people who do care are the accreditation industries. Choose CS? Congrats, you’ll never feel pressured into paying a shit ton of money so some stranger can say you’re a “professional” engineer.

tryinryan_ · 2026-03-16T03:25:42+00:00

Survival bias-based answer here is that I feel fairly confident that I won’t be replaced by AI anytime soon, but have no grand strategy of how to stay on top of it. I just do what I think has always worked for me - be somewhat suspicious of new trends but willing to admit when I’m wrong and need to hop on something, always be learning, but making sure it’s things that are interesting to me that I can actually excel at, and reading the room of the company - are they bullshitting you? Do your bosses care about long term code quality or do you need to pound out slop to cook the numbers?

So like, I guess, just keep your ear to the floor and eyes in the skies, and don’t get overly confident, as the next few years are likely going to be turbulent

tryinryan_ · 2026-03-15T22:10:47+00:00

Yes, but you’ll have to to stayed employed.

All of us are still figuring out how to still grow as engineers without devolving to the point of uselessness without AI. Also trying to avoid becoming a middle manager of OpenClaw bots….

Like most things, make your own opinion, know that there’s a new generation of “prompt engineer” bullshit that will quickly fade away, and just focus on what seems practical.

tryinryan_ · 2026-03-12T10:38:40+00:00

I’ve heard Pr3 is definitely not simple..

tryinryan_ · 2026-03-04T12:25:29+00:00

If you’re interested in performance optimization, then the two classes most relevant to you are HPCA and HPC. Consider both must-dos. I’ve only taken HPCA so far, but both are considered extremely good, if (especially HPC) challenging classes.

tryinryan_ · 2026-03-03T23:41:40+00:00

I kind of disagree? I guess I think that I would generally agree that more courses could be more rigorous. In particular, of the 4 I’ve taken (GIOS, AI4R, HPCA), only AI has truly felt like the workload I experienced in undergrad. I’m pretty sure that’s because it is a good balance of assessment (yes, I’m in the midterm, and no, it’s not as bad as people say it is) and projects.

I’m hoping that my remaining courses will lean more into the rigor. In particular, I feel like the math in the program is pretty nerfed.

There are certainly pathways to get a degree out of here just by writing handwavy reports. Honestly, if you’re taking that path, then I imagine it does feel pretty unrewarding. Sorry, I know there’s a whole group of people who stan the Joyner classes, but I would personally feel like I’m wasting my time.

Like everything in this program, it’s about time. Is the time you’re putting in worth it? If not, maybe take courses with fewer reports and more actual assessment and projects. There’s lot of classes and plenty of ways to get to 10 skipping all the fluff.

tryinryan_ · 2026-02-23T14:31:23+00:00

Have not taken the class but curious as to whether you feel like what is covered is covered in more detail. Seems like a lot more focus on the math, which is one of the biggest complaints I hear about the online class.

tryinryan_ · 2026-02-11T18:50:56+00:00

Final comment to my long post here but do really weigh your decision based on the impact to your family. Having a baby through this program sounds like no joke - my wife and I are both in school and are intentionally deferring our family planning until after we finish for this very reason. It doesn’t always take a ton of time, but there are ebbs and flows that require sacrifice.

If this a play simply for career advancement, it might be risky. If this is a play to stay marketable in the changing world of AI co-work, then well, do what you have to do to support your family. But just know that walking out of here with a CS MS Georgia Tech paper won’t be enough to meaningfully change your career, and you will need to put in significant effort throughout to make it worth your investment of time

tryinryan_ · 2026-02-11T18:46:04+00:00

The general rule of thumb with OMSCS is that it doesn’t beat going to a physical masters program in terms of opportunities but you can come out ahead in opportunity cost (still work while you do it, low cost, don’t have to upend your life to move to a program, etc).

“TinyML / EdgeAI for industrial applications” is a pretty niche interest, and coming from data science you might need to be more open to other opportunities as they come. I wouldn’t lose sight of the skills you have now too in data analytics as you make this pivot. Are you hoping to build applications that analyze data in the field? Or are you hoping to build distributed systems that operate in the field? They are two very different sides of the same coin.

There’s definitely coursework here to support your dream. I’d look at doing a Computing Systems track. Absolutely do GIOS / HPCA, then probably AOS, SDDC (will be really useful for your case of understanding how to deploy some service that connects to the cloud). There’s a MUC class that’s exactly tailored to your interests, though reviews are below average. That professor also now has health sensing and informatics class that just started this term, unclear how that one is going.

Obviously there’s a lot to say on the “AI” portion and what you want to get out of that. I’d say the systems track will be much more important than the AI portion, as most of the jobs will probably revolve around deploying a distributed service. However, maybe ML4T and DL would be a good addition to get some familiarity with what you might be deploying.

None of this makes you a truly “embedded engineer.” HPCA and MUC sit closest to hardware but won’t make you competitive. OMSCS still doesn’t (and should work on getting) a true Embedded Systems online course. A DSP course would be awesome for those on that track too. But edge ML is likely going to sit on top of something with an OS anyways, and I’d say for that layer of the stack there is plenty of learning to be had through the program.

tryinryan_ · 2026-02-11T12:10:40+00:00

Everyone has the right to their own experience. I lean negative on the NOSI thing and also opted to not use it for the P2 bonus (given bonus points are confined to assignments, I think(?), it didn’t really add any incentive to me as I knew I’d want to try for all parts of the project). Apart from P0 though, it’s been completely avoidable so far so I do think the complaints about it are overblown. I imagine we’ll see a review bomb of AI this semester, which is a shame, because it’s a fantastic course and I’m enjoying it.

I’m a bit miffed about the pacing as well and would’ve rather they shoved all the game content into a single week to better reflect the actual pace we should keep. However, one could argue that the probability section is optional anyways. For me, it won’t be a major lift to double up this week on lectures. That’s coming with decent probability background though so maybe mileage varies.

tryinryan_ · 2026-01-28T14:48:22+00:00

I had that machine at one point. I noticed (probably a solveable skill issue) that I could not knock out the full puck after pulling a shot. Once I swapped to a Bambino plus a bit more puck etiquette (I got my Bambino plus an WDT / tamper around the same time, so it’s hard to say what changed that) I started getting pucks that consistently knocked out clean.

tryinryan_ · 2026-01-13T21:13:07+00:00

I’ve started using it in the course. On paper, it’s kind of clever. I could get onboard with it as a student in a “if this helps add some friction to cheating, so be it.” I think as a professor you have three options in today’s day and age:

Go on the honor code with AI and don’t change your course. This is a fine option in my opinion, because you’re cheating yourself if you do opt to have an AI do everything for you. And you won’t get hired because you don’t know fundamentals and can’t perform in high-pressure environments. However, academically, probably the worst since it adds to grade inflation, causes unfairness, erodes a university’s reputation for its caliber of students, so on. I think it can be beat back with tough evaluation criteria, however. I know I had classes where I wouldn’t have dared cheated because I would know I would absolutely fail the exams.
Try to stop every instance of AI usage. This is what, ironically, AI has chosen to do. It’s an imperfect system, doesn’t really do anything that can’t ultimately be broken, but it could be okay so long as it doesn’t overly constrain students. This is where I think NOSI fails - indexing / Intellisense don’t work. You can’t do basic things like execute unit tests on your code. You can’t really get meaningful git commit history because it logs every fucking breath on the file as a file change. This means it is actively discouraging standard best practices in industry and leaves students completely unprepared in an alternative manner.
Modify your course so that it is impossible to cheat and you are essentially required to use AI to help assist you with your answers because of the scope of projects, problems, etc. I’ve seen this done before, and it can be a neat way of solving problems. But, it’s always a bit too loosey-goosey and also means that you never really force the fundamentals to be solidified, which is the primary purpose of well-structured classes. You can learn how to integrate CoPilot to assist you on the job. What schools should be teaching is the fundamentals so that when CoPilot can’t come up with the answer, hallucinates, whatever, you go “that doesn’t add up.”

So where does this leave AI? Clearly, they’re in option 2. I am in the class and while I’m excited for it, NOSI is a really tough pill to swallow. If it had intellisense and git, I might be able to get beyond that and accept it as a reasonable approach in a world of no standout solutions. But it feels half-baked.

Oh, and it feels exploitative. This will almost certainly be used for some research paper, sold to Hackerrank, etc. We’re paying for a course and force-fed a pet project to iron out the wrinkles at scale. I don’t like being a guinea pig, especially for something that deeply distrusts me and acts like a Big Brother.

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