In terms of performance/power or per dollar, GPUs are only good at graphics - if you're doing deep learning, for instance, GPGPU will lose to almost any other accelerator, Google's TPU is one example, FPGAs are another, a DSP designed for deep learning by CEVA, Tensilica or Synopsys are a third, and by "lose" I mean factors of 5-10x at least, assuming the same process node and operating environment, especially when deploying networks as opposed to training them. But deep learning is just one example, it'll be the same story elsewhere. The strength of GPGPU is high availability and a relatively simple & convenient programming model relatively to other accelerators.
Interesting, could you please elaborate on why you think this? Any data you know of on the subject? What are better alternatives in you view?
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