Driving Compute And Storage Scale Independently
While legacy monolithic applications will linger in virtual machines for an incredibly long time in the datacenter, new scale-out applications run best on new architectures. …
While legacy monolithic applications will linger in virtual machines for an incredibly long time in the datacenter, new scale-out applications run best on new architectures. …
Over the last year, stories pointing to a bright future for deep neural networks and deep learning in general have proliferated. …
We have been convinced for many years that machine learning, the kind of artificial intelligence that actually works in practice, not in theory, would be a key element of the next platform. …
Deep learning could not have developed at the rapid pace it has over the last few years without companion work that has happened on the hardware side in high performance computing. …
It is hard to find a more hyperbolic keynote title than, “A New Computing Model” but given the recent explosion in capabilities in both hardware and algorithms that have pushed deep learning to the fore, Nvidia’s CEO keynote at this morning’s GPU Technology Conference kickoff appears to be right on target. …
Just because computing and storage are commodities does not mean, by any stretch of the imagination, that they are inexpensive. …
Breaking into the datacenter with a new chip architecture is probably more difficult than getting by the security in a modern glass house and literally breaking into it, either physically or digitally over the wire. …
The high end of the computing industry has always captivated us, and we still find the forces at work in the upper echelons of the datacenters of the world, and the hardware and software that is created to run the largest and most complex workloads found there, fascinating. …
If it was as easy as global replacing a bunch of MIPS cores with a bunch of ARM cores, then network chip makers Cavium and Broadcom would already have long since put their respective “ThunderX” and “Vulcan” 64-bit ARM server processors into the market. …
Stanford University PhD candidate, Song Han, who works under advisor and networking pioneer, Dr. …
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