Nvidia made a lot of big bets to bring its “Pascal” GP100 GPU to market and its first implementation of the GPU is aimed at its Tesla P100 accelerator for radically improving the performance of massively parallel workloads like scientific simulations and machine learning algorithms. …
As a former research scientist at Google, Ian Goodfellow has had a direct hand in some of the more complex, promising frameworks set to power the future of deep learning in coming years. …
“AI is transforming the entire world of technology. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data. …
If the last year of stories here from research labs at the forefront of deep learning hasn’t made it clear, the accelerator of choice for training the models that will feed the next generation of speech and image recognition (not to mention a wealth of other application areas) is certainly GPUs. …
For Google, Baidu, and a handful of other hyperscale companies that have been working with deep neural networks and advanced applications for machine learning well ahead of the rest of the world, building clusters for both the training and inference portions of such workloads is kept, for the most part, a well-guarded secret. …
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. …
For more than a decade, graphics processor maker Nvidia has been championing the adoption of GPU accelerators as heavy-lifting compute engines for an increasing array of applications that can take advantage of the parallel processing inherent in a GPU. …
Although GPUs have found a wider market in high performance computing in recent years, an emerging market is exploding in deep learning and computer vision with companies like Baidu and others continuing to push the processing speed and complexity envelope. …
During his sabbatical at Twitter, GPU researcher and graph analytics whiz, UC Davis professor John Owens, made a list of the elements the social network might want to consider as it built out its recommendation and other real-time services. …
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