For the past 7 years, it has not been Intel but NVIDIA that has pushed the frontier of Moore’s processor performance/price curve. For a 2016 data point, consider the NVIDIA Titan GTX. It offers 10^13 FLOPS per $1K (11 trillion calculations per second for $1200 list price), and is the workhorse for deep learning and scientific supercomputing today. And they are sampling much more powerful systems that should be shipping soon. The fine-grained parallel compute architecture of a GPU maps better to the needs of deep learning than a CPU. There is a poetic beauty to the computational similarity of a processor optimized for graphics processing and the computational needs of a sensory cortex, as commonly seen in neural networks today.

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