Comment by threecheese

I wonder, is the dual-use of GPUs a significant engineering bottleneck for Nvidia et al? My understanding is that for libraries like Tensorflow and PyTorch, much of the complexity is supporting the many-to-many mapping of model structures to GPU silicon in a way that reduces “compile time”, with all the tradeoff landmines you might expect there. I would imagine that abstracting this at the hardware layer is really valuable. Are these non-GPU architectures the future of machine learning, and can we expect the big vendors to start competing here or is this a business risk for them? (robbing Peter to pay Paul, like Google investing in AI that cannibalizes their search revenue)