At Intel's recent AI Day, the chipmaker previewed a series of future products that it intends to use to unseat GPUs as the de facto standard for machine learning. The one-day event was Intel's most assertive pronouncement of its intentions to become a major player in the artificial intelligence market. Perhaps the most surprising element of the roadmap is the productization of the Nervana Engine, the machine learning chip that Nervana engineers were working on prior to Intel's acquisition of the company earlier this year. Given Intel's devotion to Xeon Phi, we at TOP500 News were more than a little skeptical that the company would ever commercialize the Nervana designs. But that is apparently the plan.

At the MIT EmTech Digital conference, startup Nervana announced plans to design and build a custom ASIC processor for neural networks and machine learning applications that the company's CEO, Naveen Rao, claims will run 10 times faster than graphic processor units (GPU). The news comes after Google last week announced it had secretly deployed its neural network and machine-learning-tailored processors in its data centers about a year ago. The company reported that its custom processor had improved performance by an order of magnitude. GPUs have become synonymous with machine learning. Interest in machine learning exploded a few years ago when Alex Krizhevsky, a student of artificial intelligence (AI) luminary Geoff Hinton at the University of Toronto, proved that machine learning systems could be trained on economically priced GPU hardware.