To a certain way of looking at it, Nvidia has always been engaged in the high performance computing business and it has always been subject to the same kinds of cyclical waves that affect makers of supercomputers and enterprise systems. …
Earlier in this decade, when the hyperscalers and the academics that run with them were building machine learning frameworks to transpose all kinds of data from one format to another – speech to text, text to speech, image to text, video to text, and so on – they were doing so not just for scientific curiosity. …
History and economics – as if you could separate the two – are burgeoning with examples of products being developed for one task and then being used, perhaps after some tweaking, for an entirely new and usually unexpected task. …
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. …
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