HPE Chases Deep Learning With GPU Laden Apollo Systems

With machine learning taking off among hyperscalers and others who have massive amounts of data to chew on to better serve their customers and traditional simulation and modeling applications scaling better across multiple GPUs, all server makers are in an arm’s race to see how many GPUs they can cram into their servers to make bigger chunks of compute available to applications.


Wider Net Cast Over Deep Learning On GPUs

The future of hyperscale datacenter workloads is becoming clearer and as that picture emerges, if one thing is clear, it is that the content is heavily driven by a wealth of non-text content—much of it streamed in for processing and analysis from an ever-growing number of users of gaming, social network, and other web-based services.


Deep Learning Pioneer Pushing GPU Neural Network Limits

Back in the late 1980s, while working in the Adaptive Systems Research Department at AT&T Bell Labs, deep leaning pioneer, Yann LeCun, was just starting down the path of implementing brain-inspired machine learning concepts for image recognition and processing—an effort that would eventually lead to some of the first realizations of these technologies in voice recognition for calling systems and handwriting analysis for banks.