not just the chip shortage. The rise of RISC-V coincides with a couple of other events in the industry. The first is the slowing of Moore’s Law, meaning that increases in total processing power no longer comes along with each new fabrication node. The second is the meteoric rise in machine learning, demanding massive increases in processing power. https://semiengineering.com/why-risc-v-is-succeeding/
Language models and image generation make fun demos, but do we have transformative use cases that'll actually require large ML compute in the future ?
Voice recognition and translation are the only ones that comes to mind, yet don't require that much power.
Hmm,
Is there any discussion of how RISC-V designs could be incorporated into a GPU or TPU that could train deep learning systems? Your link doesn't say anything about that but it's an interesting question.
