From a software perspective, second-generation Intel Xeon Phi processors are binary compatible with x86 architecture processors including the Intel Xeon® E5 family of processors. That means you only have to modernize your code once to boost your time to train on second-generation Intel Xeon Phi processors and your existing Intel Xeon processor-based servers. By "modernization" I don't mean that you have to write ninja parallel code. We're making it easier to parallelize ML/DL code for general-purpose x86 architecture-based CPUs with tools like the popular Intel® Math Kernel Library, which includes new extensions for optimizing deep neural networks in the Intel® MKL 2017 Beta release that's available now. In addition, we're hard at work optimizing for x86 architectures on popular open-source ML frameworks such as Caffe* and Theano*. These and other efforts, even without hardware upgrades, are producing performance boosts on the order of 30 times for DL applications.