Researchers Deploy GPUs to Build World's Largest Artificial Neural Network

Researchers Deploy GPUs to Build World's Largest Artificial Neural Network 
GPU-Accelerated Machine Learning and Data Mining Poised to
Dramatically Improve Object, Speech, Audio, Image and Video
Recognition Capabilities 
LEIPZIG, GERMANY -- (Marketwired) -- 06/18/13 --  ISC 2013 -- NVIDIA
today announced that it has collaborated with a research team at
Stanford University to create the world's largest artificial neural
network built to model how the human brain learns. The network is 6.5
times bigger than the previous record-setting network developed by
Google in 2012. 
Computer-based neural networks are capable of "learning" how to model
the behavior of the brain -- including recognizing objects,
characters, voices and audio in the same way that humans do.  
Yet creating large-scale neural networks is extremely computationally
expensive. For example, Google used approximately 1,000 CPU-based
servers, or 16,000 CPU cores, to develop its neural network, which
taught itself to recognize cats in a series of YouTube videos. The
network included 1.7 billion parameters, the virtual representation
of connections between neurons. 
In contrast, the Stanford team, led by Andrew Ng, director of the
university's Artificial Intelligence Lab, created an equally large
network with only three servers using NVIDIA(R) GPUs to accelerate
the processing of the big data generated by the network. With 16
NVIDIA GPU-accelerated servers, the team then created an 11.2
billion-parameter neural network -- 6.5 times bigger than a network
Google announced in 2012.  
The bigger and more powerful the neural network, the more accurate it
is likely to be in tasks such as object recognition, enabling
computers to model more human-like behavior. A paper on the Stanford
research was published yesterday at the International Conference on
Machine Learning.  
"Delivering significantly higher levels of computational performance
than CPUs, GPU accelerators bring large-scale neural network modeling
to the masses," said Sumit Gupta, general manager of the Tesla
Accelerated Computing Business Unit at NVIDIA. "Any researcher or
company can now use machine learning to solve all kinds of real-life
problems with just a few GPU-accelerated servers." 
GPU Accelerators Power Machine Learning
 Machine learning, a
fast-growing branch of the artificial intelligence (AI) field, is the
science of getting computers to act without being explicitly
programmed. In the past decade, machine learning has given us
self-driving cars, effective web search and a vastly improved
understanding of the human genome. Many researchers believe that it
is the best way to make progress towards human-level AI. 
One of the companies using GPUs in this area is Nuance, a leader in
the development of speech recognition and natural language
technologies. Nuance trains its neural network models to understand
users' speech by using terabytes of audio data. Once the models are
trained, they can then recognize the pattern of spoken words by
relating them to the patterns that the model learned earlier. 
"GPUs significantly accelerate the training of our neural networks on
very large amounts of data, allowing us to rapidly explore novel
algorithms and training techniques," said Vlad Sejnoha, chief
technology officer at Nuance. "The resulting models improve accuracy
across all of Nuance's core technologies in healthcare, enterprise
and mobile-consumer markets."  
NVIDIA will be exhibiting at the 2013 International Supercomputing
Conference (ISC) in Leipzig, Germany this week, June 16-20, at booth
 Since 1993, NVIDIA (NASDAQ: NVDA) has pioneered the art
and science of visual computing. The company's technologies are
transforming a world of displays into a world of interactive
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For further information, contact:
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NVIDIA Public Relations
(408) 562-7226 
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