Chip Fights: Nvidia Takes Issue With Intel’s Deep Learning Benchmarks

Intel recently published some Xeon Phi benchmarks, which claimed that its “Many Integrated Core” Phi architecture, based on small Atom CPUs rather than GPUs, is significantly more efficient and higher performance than GPUs for deep learning. Nvidia seems to have taken issue with this claim, and has published a post in which it detailed the many reasons why it believes Intel’s results are deeply flawed. GPUs Vs. Everything Else Whether they are the absolute best for the task or not, it’s not much of a debate that GPUs are the mainstream way to train deep learning neural networks right now. That’s because training neural networks requires low precision computation (as low as 8-bit), and not high-precision computation, for which CPUs are generally built. Whether GPUs will one day be replaced…


Link to Full Article: Chip Fights: Nvidia Takes Issue With Intel’s Deep Learning Benchmarks