TensorFlow, probably the most popular of the dozen or so deep learning frameworks, is typically used to develop neural networks on small or medium-sized clusters, and sometimes on just a single GPU-accelerated node. …
Generative adversarial neural networks are the next step in deep learning evolution and while they hold great promise across several application domains, there are major challenges in both hardware and frameworks. …
Machine learning, arguably the most interesting and successful form of artificial intelligence, only worked because of the confluence of enormous amounts of data to train models and tremendous amounts of compute to chew on that data with many-layered statistical algorithms. …
In the deep learning inferencing game, there are plenty of chipmakers, large and small, developing custom-built ASICs aimed at this application set. …
It might be a bit early to call generative adversarial networks (GANs) the next platform for AI evolution, but there is little doubt we will hear much more about this beefed up approach to deep learning over the next year and beyond. …
This story has been temporarily removed. If you want to learn more about Inspur’s machine learning hardware, check it out here. …
Every important benchmark needs to start somewhere.
The first round of MLperf results are in and while they might not deliver on what we would have expected in terms of processor diversity and a complete view into scalability and performance, they do shed light on some developments that go beyond sheer hardware when it comes to deep learning training. …
We have spent the past several years speculating about what the “Summit” supercomputer built by IBM, Nvidia, and Mellanox Technologies for the US Department of Energy and installed at Oak Ridge National Laboratory might be. …
IBM and the other vendors who are bidding on the CORAL2 systems for the US Department of Energy can’t talk about those bids, which are in flight, and Big Blue and its partners in building the “Summit” supercomputer at Oak Ridge National Laboratory and “Sierra” at Lawrence Livermore National Laboratory – that would be Nvidia for GPUs and Mellanox Technologies for InfiniBand interconnect – are all about publicly focusing on the present, since these two machines are at the top of the flops charts now. …
Nvidia co-founder and chief executive officer, Jensen Huang, would be the first one to tell you that the graphics chip maker was an unintended innovator in supercomputing, that what the engineers who created the first Nvidia GPUs were really trying to do was enable 3D video games. …
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