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> Are you saying that GPUs invest area on floating-point multipliers because FLOPS are an important marketing metric? Yes. That is precisely what i'm saying.

> Are you saying that GPUs invest area on floating-point multipliers because FLOPS are an important marketing metric? Yes. That is precisely what i'm saying. If i'm mistaken in saying that that's one thing, but as far as it being what i'm saying, it very much is. It's been an important guiding principle for some time now in the project, that recent chips--including FPGA's--tend to have hard IP for floating point multiplication. Now spending a lot of chip area on getting more FLOPS is not necessarily a bad decision if there is no alternative for achieving fast matrix multiplication. Almost any method is sensible if there was no better alternative available when the decision to use that method was made. In addition, fgemm only really makes sense when matrices contain over 1000 elements per row or column, not sure how much more than 1000 per vector but more than that. Small and in particular small and dense matrices are still best multiplied exactly the way GPUs multiply them, with many floating-point multiplier circuits in parallel. It's not stupid in the least. Yeah so NVidia n Google have the same business model i'm going for, Google having TPUs in its datacenters that do work that cannot be reverse engineered. Google does not sell TPUs. You can use them by sending Google the work, and you'll benefit from much lower cost and faster speed. NVidia has a similar offering, just not as well-known. That's the correct business model in my analysis, and what fgemm will sell. Sell the work.

imtringued 3y ago
What about us peasants who need multiplication to actually get work done instead of playing FLOPs status games? Not everyone is bottlenecked on something as specific as matrix multiplication. Also the claims about huge amounts of area being dedicated to multiplication are false. ALU size is mostly irrelevant.
- adgjlsfhk1 3y ago
  games are like 60% matrix multiplication (especially with ray tracing)
david-gpu 3y ago
> Google having TPUs in its datacenters that do work that cannot be reverse engineered Help me understand: TPUs cannot be reverse engineered because the user doesn't have access to the physical device, but other devices like GPUs can? Can you show some examples of reverse-engineering of GPUs that has been performed on the basis of having physical access to the dies? Are you aware of any reverse engineering done on them using other means? How much has this reverse engineering prevented e.g. NVidia from being financially successful? Finally, since patents are freely available to the public once they have been granted, does that nullify some concerns regarding reverse engineering? I'm not an entrepeneur, so take this with a fistful of salt, but having worked at places like NVidia, I would never try to compete head to head with them, as a startup. Very few semiconductor startups achieve any success, and the ones that do start by finding a very particular market niche where the established players aren't even trying to play. Again, I wish you good luck.

imtringued 3y ago

What about us peasants who need multiplication to actually get work done instead of playing FLOPs status games? Not everyone is bottlenecked on something as specific as matrix multiplication. Also the claims about huge amounts of area being dedicated to multiplication are false. ALU size is mostly irrelevant.

adgjlsfhk1 3y ago
games are like 60% matrix multiplication (especially with ray tracing)

david-gpu 3y ago

> Google having TPUs in its datacenters that do work that cannot be reverse engineered Help me understand: TPUs cannot be reverse engineered because the user doesn't have access to the physical device, but other devices like GPUs can? Can you show some examples of reverse-engineering of GPUs that has been performed on the basis of having physical access to the dies? Are you aware of any reverse engineering done on them using other means? How much has this reverse engineering prevented e.g. NVidia from being financially successful? Finally, since patents are freely available to the public once they have been granted, does that nullify some concerns regarding reverse engineering? I'm not an entrepeneur, so take this with a fistful of salt, but having worked at places like NVidia, I would never try to compete head to head with them, as a startup. Very few semiconductor startups achieve any success, and the ones that do start by finding a very particular market niche where the established players aren't even trying to play. Again, I wish you good luck.