AI/ML accelerators in these things are mainly for inference purposes - training most NN's takes considerable time and power (watts and TOPS), whereas inference using a fully trained NN can be extremely efficient even on these relatively small ASIC's.I am still curious as to why anyone needs AI enhancements in iGPUs, phone SoCs, and the like. What's the play here? Run massive AI learning clusters with these things?
Take this as rumors for now...
Small Zen2 APU:
Four Zen2 cores
Three Vega CUs or Two RDNA2 Poly-CUs.
6nm(N6-AMD) Refined/Custom process node(weakened up-port of N5A which is the 5nm custom node AMD will be using)
6W TDP/12W Slow PPT/18W Fast PPT is the target for all SKUs.
With those timings and memory clocks the latency is somewhat believable.Maybe that guy's memory kit win silicon lottery. But still it might be a bug of recognize latency of Renoir, cuz Renoir is not fully support by AIDA64 yet...... But OTOH the FCLK is pretty real.
Though this is an old topic that had no update since last year but I think this topic is still suitable to place some leaks here.
View attachment 24190
this guy running 8C Renoir with fclk2100 memory, seems they implied 4000mhz is easy.
emmm ok there is another one in same thread:
(the guy who leak this one said he turned off igfx before testing AIDA64 then result in 49ns)
View attachment 24192
Yes, but only if Renoir APU/AM4 will jump into any older AM4 motherboards.Wow.
Official answer is no.AMD has plans to make Sempron or Duron come back?
It's possible, that Renoir similarly to Raven Ridge include some changes from upcoming gen (in this case Zen 3) despite of using officially Zen 2 architecture?
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