Statistic of microsoft skype translator over the world

#Statistic of microsoft skype translator over the world software#

It wants a platform that can support many different kinds of intelligent services and applications вЂ“ search (text and image), computer vision, natural language processing and translation, recommendation engines вЂ“ basically anything that that involves advanced analytics and pattern matching driven by web-scale datasets.ДЇor such a diverse set of offerings, FPGA technology has an attraction beyond any CPU, GPU or purpose-built ASIC: reconfigurability. Microsoft, though, as a result of its broad range of offerings in Windows, Office 365, Azure, Cortana, and Skype, sees the AI space differently. Of course, if youвЂ™ve been tracking NVIDIA about their aspirations in web serving AI, youвЂ™ll know they offer a 50-watt P4 GPU aimed at roughly the same application set. That makes it relatively easy to slide one or more of these devices into a web server, which tend to be power-constrained because of the scale of the datacenters they must run in. More importantly, they can do so in a rather small power envelope вЂ“ on the order of 25 watts per chip. Such a capability requires a much larger infrastructure, one that can evaluate millions of such requests in real time spread across a global cloud.Д¬onveniently, FPGAs are well suited for such workloads, offering the type low-precision (8-bit and 16-bit) capabilities that these deep learning and other AI algorithms require for optimal efficiency. The FPGA build-out looks to be primarily aimed at inferencing (evaluating already trained neural networks) for applications such as image recognition, speech recognition, language translation and so on. And it also offers GPU servers to the public in its Azure offering for things like genome sequencing and other scientific workloads. At this point, the company still uses smaller clusters GPUs to performВ the more computationally intense training phase offline. It should be said that, at least for the time being, Microsoft doesnвЂ™t appear to be aiming the FPGA-powered infrastructure at the training of these neural nets. From there Microsoft experimented with convolutional neural networks, using FPGAs to train image classification networks, achieving performance per watt on par with that of the best GPUs at the time.

Employing FPGAs programmed for that one piece of code, the company was able to double the throughput on the algorithm at a cost of just 10 percent more power per server. MicrosoftвЂ™s first serious foray into FPGA came with accelerating BingвЂ™s search ranking algorithm, a cloud application used by hundreds of millions of people. Just as in traditional high performance computing, multicore CPUs werenвЂ™t keeping up with demand.Дoug Burger with Microsoft-designed FPGA card

The rationale was that CPU evolution, a la MooreвЂ™s Law, was woefully inadequate in keeping up with the demands of these new hyperscale applications. The effort began in earnest in 2011, when the company launched Project Catapult, the R&D initiative to design an acceleration fabric for AI services and applications.

The FPGA build-out was the culmination of more than five years of work at Microsoft to find a way to accelerate machine learning and other throughput-demanding applications and services in its Azure cloud. The announcement was made by Microsoft CEO Satya Nadella and engineer Doug Burger during the opening keynote at the Ignite Conference in Atlanta. Microsoft has revealed that Altera FPGAs have been installed across every Azure cloud server, creating what the company is calling вЂњthe worldвЂ™s first AI supercomputer.вЂќ The deployment spans 15 countries and represents an aggregate performance of more than one exa-op.

#Statistic of microsoft skype translator over the world software#

Software giant bets the (server) farm on reconfigurable computing