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ASICs/Google TPU like things for AI inference/training will be easy to replicate and will have 95% of the power of existing solutions or maybe even improvements
by lajamerr 4y ago
ASICs/Google TPU like things for AI inference/training will be easy to replicate and will have 95% of the power of existing solutions or maybe even improvements. Because they are simple in design.
As far as dedicated GPUs. There is so much additional work in the software side/driver side of thing unless there was a big concerted effort by a single company to build a solid foundation I see it as unlikely. Look at Intel struggling to get their GPUs out there.
Dedicated accelerated cards just for ML though? I believe some already exist/more will come.
- noogle 4y agoAnd the biggest push to invest in re-inventing a software stack is a legal ban on access to the existing software. If previously building a separate CUDA-like framework competed against paying a bit more to Nvidia, now this decision is infinitely simpler, since using Nvidia products is just out of the question.
- throwaway4good 4y agoYep. This ban basically will force out an (widely used) alternative to CUDA and break Nvidia's defacto monopoly on highend AI compute. Quite a gift to the Chinese if you ask me but US policy makers seem blind to this.