Google's TPU claims to have a >10x perf/w advantage over GPUs (they state competitors, but GPUs are the defacto standard) and thats frankly more important that raw throughput at scale.
If Intel can ape Nvidia at deep learning then it is a big growth opportunity for them.
They don't state flops because they don't do floats. The nervana chip has some weird fixed point format that they think works better for deep learning. I've heard similar noise out of Google (the other TPU company ...), so I wouldn't be surprised if we'll see the same in many of the specialized chips that people will build for deep learning.
I really don't think CUDA support is important for nervana's offering. They think of themselves as the Apples of deep learning - they want to offer an integrated stack from the chip all the way to the APIs. The way most people use deep learning you don't really need to know CUDA, you just need to use a library that is fast. So it's enough that nervana's engineers know how to write deep learning libs for their own chip. Furthermore I can't see Intel caring that much about CUDA support, since CUDA is owned by Nvidia.
What I've heard about the chip makes it sound really exciting. Many of the trade-offs in deep learning are different from the ones you do in graphics, so specialized hardware makes sense.
