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If one can make a board with enough cheap high performance CPUs, it may be good for neural nets. The main reason we use NVIDIA hardware is because it's cheaper
by etaioinshrdlu 8y ago
If one can make a board with enough cheap high performance CPUs, it may be good for neural nets. The main reason we use NVIDIA hardware is because it's cheaper per FLOP.
- Alupis 8y ago> The main reason we use NVIDIA hardware is because it's cheaper per FLOP ... well... and largely because most neural network software only supports CUDA and/or has rudimentary OpenCL support. Nvidia hardware isn't really cheaper per FLOP in all cases, AMD and Nvidia still leapfrog each other back and forth.
- etaioinshrdlu 8y agoSure, but every framework has full support for CPUs.
- Alupis 8y agoThat's true to a point... latest versions of TensorFlow, for example, have caused a few issues with instruction sets that not all CPU's (even produced today) have.
- walterbell 8y agoIs that because of CPUs that are only available at major cloud providers?
- Alupis 8y agoNo, not entirely. AVX instruction set, for instance, seems to only be on select Intel and AMD cpu's, even modernly manufactured ones. TensorFlow changed to requiring AVX since version 1.4 (Nov 2017), which has caused grief for many users... even those with recent/performant systems.
- antoineMoPa 8y agoA board with many CPUs would be nice, but communication between many CPUs is slower than communication within one chip (GPU).
- etaioinshrdlu 8y agoMost NN tasks are embarrassingly parallel.
- deleted 8y ago[deleted]
- samfisher83 8y agoGPU will generally have more cores. Its why a GPU was made to do is bunch of math: Matrix multiples, dot products, etc.
- IshKebab 8y agoYeah we use graphics cards because they are cheaper per FLOP than a bunch of CPUs. Using cheap CPUs isn't really going to change that.