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I agree about Xeon Phi, but how is arm not many core enough? Back of the envelope.. a GTX 1080 has 2560 cores, TDP 300W, 1.6 GHz, so ~12 compute thread GHz/W.
by marmaduke 9y ago
I agree about Xeon Phi, but how is arm not many core enough?
Back of the envelope.. a GTX 1080 has 2560 cores, TDP 300W, 1.6 GHz, so ~12 compute thread GHz/W. Dual 48-core ARM boards, 4x32 SIMD, TDP 100W, 2.5 Ghz, so ~10 compute thread Ghz/W. (this is assuming IPC and price are similar)
So, for SIMD enabled workloads, there's potential to be in the same ballpark. For non SIMD or mixed workflows, GPU isn't even an option. For HPC in general (not deep learning specifically), arm may be a better long run bet that GPU.
- deepnotderp 9y agoThere's a variety of even more efficient MIMD processors. Eg, rex neo, adapteva epiphany, etc. The reason I don't see ARM being very competitive is that ARM processors have no discernible advantage compared to a clean slate ISA design. What exactly does ARM offer that the Rex Neo doesn't? By contrast, the Neo is far more efficient, doesn't waste energy on OoO, has no caches, making it far more efficient (especially in terms of data movement, which is a big deal) and has the advantage of having a simpler decoder.
- marmaduke 9y agoI haven't heard of that stuff to be honest, so it's more likely in my case to get stuff into an FPGA than considering about a new ISA...