For example, my parents and all their friends weren't well off enough to afford private university, but majors at public university were limited by the government based on projected need with slots offered to only the highest test scores. They couldn't test high enough to secure a public university slot for accounting, art, architecture, education, medicine, and trades such as automotive repair or plumbing. So they all ended up with computer science and engineering degrees. Fully paid for by the government.
They basically treat the semiconductor industry like how the US treats its defense industry.
I think is a good example of what happens when you do that.
It seems intuitive to me that as war technology progresses, number of humans becomes less of a factor in military strength, but I know so little about this area that I couldn't guess how greatly alternative educational opportunities impact military applications, nor at what point in the past/future the scale might tip between wanting policies that push more into armed forces vs. no longer being so important (and I assume it would be different for different countries, too).
But I do believe good, free education should be a key part of any country, regardless of whether it helps national défense or not. If that drives up the cost of recruiting people to the armed forces then fine - I'm no fan of them in general, but if people are going to risk their lives potentially in wars then it shouldn't be because their choices were limited to that vs a life of poverty.
https://books.google.com/books?id=C6VFJIbxX7MC&lpg=PA71&ots=...
The only chance to sit it out would be a giant technological leap as in quantum computing, I just don't see that leap.
I'd like to see it, but I'm not sure anyone can catch up now.
However, I think NVIDIA is still vulnerable—but against AWS/GCP/Azure, not Intel/AMD.
My opinion is that deep learning is moving to the cloud. That's a bigger conversation with a lot of nuances, but if you take that basic assumption, then the development of ASICs like TPU/Inferentia become a big threat to Nvidia.
If the biggest buyers of chips in deep learning are the clouds, and the clouds are increasingly developing their own chips for deep learning, Nvidia is in a tough spot. They'll always have a place among labs that use their own machine, and of course, Nvidia's business is bigger than machine learning, but in general I think the clouds are a real threat.
One of Nvidia's actual moats is their system building competency, which AMD lacks. They can sell you a box / a whole server room configuration, since their acquisition of Mellanox together with network equipment.
The cost of Hardware Development is the main cost contribution.
Which I think is not true with regards to both GPU and GPGPU computing. The major cost for GPU is Drivers, and CUDA for GPGPU. i.e It is Software.
Unlike ARM where AWS/GCP/Azure can make their chips and benefits from the Software ecosystem already in place for ARM, there is no such thing on GPU. Drivers and CUDA is the biggest moat around Nvidia's CPU. And unless Developers figure out a way to drive the cost of DL and Drivers down, there is no incentives to switch away from Nvidia's ecosystem.
That is why I am interested to see how Intel tackle this area. And if History will repeat itself again in the Voodoo, Rage 3D and S3 Verge era.
If AMD could provide a backend for the most popular frameworks then they could skip over the CUDA patent issue completely.
The real problem is that it seems like AMD’s not investing substantially in software teams to make it happen.
https://github.com/pytorch/pytorch/issues/10657
That is the state of Pytorch support for AMD GPUs.
I do. Not everything you can do with a CUDA card is deep learning. In fact that's just one of many applications.
There has been some progress, but PyTorch still isn't fully functional with ROCm yet and that feels like a good litmus test.
Really do not want just one players. And hope the high level plays have more completion.
Still interest in Taiwan part. Purely from economic point of view. How secure are we ok that front, if all eggs are in one basket. Hk is fallen. Taiwan or South China Sea is in play. That will affect the supply chain.
Intel is launching a GPU/Deep learning accelerator, Huawei is thinking about launching a GPU. Pytorch and Tensor flow work well enough on AMD GPUs. There are also custom deep learning ASICs from Google. There is simply too much competition at this point for CUDA to continue to be the standard.
Marketing fad or not, it’s not a bad business to be in.
It is barely starting. In 10 and 20 years it will be huge.
Nvidia will have to validate and launch all of it’s PCIe4 on Epyc/Ryzen processors, so it’s not like AMD won’t benefit from the Deep Learning hype
It also doesn’t help that AMD has stopped supporting ROCm for the current consumer GPUs.
https://www.bloomberg.com/news/articles/2020-07-22/softbank-...