Notes on AI Hardware - Benjamin Spector | Stanford MLSys #88 

I blew through a ton of different topics in the course of the talk, so it really depends what you're looking for. If you want more on making the most of an H100, NVIDIA has fairly good docs on both the CUDA programming model as well as the specific features of the H100, but actually using them can be tricky, so your best bet is probably to read the CUTLASS repo and see how they do things. If you want more on hardware design, I'm not sure there are great alternatives to taking a class. Hardware design seems to me like an awful lot of work -- writing good RTL is hard enough, but the whole EDA stack is a bit of a nightmare. If you want more on semiconductor manufacturing, I'd highly recommend the Asianometry YT channel, which has a lot of really excellent content. Otherwise, some of my main sources for this talk were SemiAnalysis ($500/yr, but I like it enough that I pay for it even from a grad student stipend), Bill Dally's HC2023 talk, and various coursework, particularly 6.172 from MIT for performance engineering. (It's on OCW at ocw.mit.edu/courses/6-172-performance-engineering-of-software-systems-fall-2018/video_galleries/lecture-videos/ and while it's focused on CPU performance engineering many of the principles apply across both.) Hope this helps!

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