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And there you have it. Perhaps the greatest thing to happen to RISC-V since the invention of the FPGA :-). I never liked Softbank owning it, but hey someone has to. Regarding the federal investment in FOSS thread that was here perhaps CPU architecture would be a good candidate.
The next Apple machine I am going to buy will be using RISC-V cores.
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The problem is more that AMD is sleeping in regard of GPU AI and good software interfaces.
Hardly sleeping, they have a fraction of the revenue and profit of NVidia and Intel. Revenue was half of Nvidia, profit was 5% of NVidia. Intel is even bigger. They only have so much in the way of R&D money.
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Have you read the press release at all? It's too early to judge this now. ARM will stay in Cambridge and Nvidia wants to invest in this place.
Corporations say these types of things with every acquisition. It might be true initially and superficially, but that's all.
On the bright side, this could end up being a big boost for RISC-V.
Talking points from the founders of Arm & Nvidia: https://www.forbes.com/sites/patrickmoorhead/2020/09/13/its-... > Huang told me that first thing that the combined company will do is to, “bring NVIDIA technology through Arm’s vast network.” So I’d expect NVIDIA GPU and NPU IP to become available quickly to smartphone, tablet, TV and automobile SoC providers as quickly as possible. > Arm CEO Simon Segars framed it we…
Bye bye ARM Mali =(
Now, every reference-implementation ARM processor manufactured will fund GeForce desktop products, datacenter/enterprise, etc as well.
NVIDIA definitely needs something like this in the face of the new Samsung deal, as well as AMD's pre-existing console deals.
Can anyone point me who would be competing with Nvidia in AI Market ?
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>long standing tech companies like FAANMG why are people going out of their ways to avoid the obvious and intuitive "FAGMAN" acronym?
Netflix really doesn’t belong in the same category as the others. It’s big but not as big, and it isn’t a sprawling conglomerate. Clearly “FAGMA” is the best acronym.
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Experience, for one. TPUs are dominating MLPerf benchmarks. That kind of performance can't be dismissed so easily. GPT-2 was trained on TPUs. (There are explicit references to TPUs in the source code: https://github.com/openai/gpt-2/blob/0574c5708b094bfa0b0f6df... ) GPT-3 was trained on a GPU cluster probably because of Microsoft's billion-dollar Azure cloud credit investment, not because it was the best choice.
no they are not. Go read recent MLPerf results more carefully and not Google’s blogpost. NVIDIA won 8/8 benchmarks for publicly available SW/HW combo. Also 8/8 on per chip performance. Google did show better results with some “research” system which is not available to anyone other then them yet.
For MLPerf 0.7, it's true that Google's software isn't available to the public yet. That's because they're in the middle of transitioning to Jax (and by extension, Pytorch). Once that transition is complete, and available to the public, you'll probably be learning TPU programming one way or another, since there's no other practical way to e.g. train a GAN on millions of photos.
You'd think people would be happy that there are realistic alternatives to nVidia's monopoly for AI training, rather than rushing to defend them...
Talking points from the founders of Arm & Nvidia: https://www.forbes.com/sites/patrickmoorhead/2020/09/13/its-... > Huang told me that first thing that the combined company will do is to, “bring NVIDIA technology through Arm’s vast network.” So I’d expect NVIDIA GPU and NPU IP to become available quickly to smartphone, tablet, TV and automobile SoC providers as quickly as possible. > Arm CEO Simon Segars framed it we…
Bye bye ARM Mali =(