Deep Learning Chip Upstart Takes GPUs to Task
Update – 8/9/16 1:00 p.m. Pacific – Not even 24 hours after this story was posted Intel bought Nervana Systems. …
Update – 8/9/16 1:00 p.m. Pacific – Not even 24 hours after this story was posted Intel bought Nervana Systems. …
As we have written about extensively here at The Next Platform, there is no shortage of use cases in deep learning and machine learning where HPC hardware and software approaches have bled over to power next generation applications in image, speech, video, and other classification and learning tasks. …
Intel has finally opened the first public discussions of its investment in the future of machine learning and deep learning and while some might argue it is a bit late in the game with its rivals dominating the training market for such workloads, the company had to wait for the official rollout of Knights Landing and extensions to the scalable system framework to make it official—and meaty enough to capture real share from the few players doing deep learning at scale. …
Nvidia wants for its latest “Pascal” GP100 generation of GPUs to be broadly adopted in the market, not just used in capability-class supercomputers that push the limits of performance for traditional HPC workloads as well as for emerging machine learning systems. …
AMD gets a lot of credit for creating Accelerated Processing Units that merge CPUs and GPUs on a single package or on a single die, but Intel also has a line of chips Core and Xeon processors that do the same thing for workstation and server workloads. …
Having made the improbable jump from the game console to the supercomputer, GPUs are now invading the datacenter. …
As we have noted over the last year in particular, GPUs are set for another tsunami of use cases for server workloads in high performance computing and most recently, machine learning. …
One of the breakthrough moments in computing, which was compelled by necessity, was the advent of symmetric multiprocessor, or SMP, clustering to make two or more processors look and act, as far as the operating system and applications were concerned, as a single, more capacious processor. …
This month Nvidia bolstered its GPU strategy to stretch further into deep learning, high performance computing, and other markets, and while there are new options to consider, particularly for the machine learning set, it is useful to understand what these new arrays of chips and capabilities mean for users at scale. …
Pattern analytics, deep learning, and machine learning have fueled a rapid rise in interest in GPU computing, in addition to GPU computing applications in high performance computing (HPC) and cloud-based data analytics. …
All Content Copyright The Next Platform