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| Yanzhi Wang, assistant professor of electrical and computer engineering, has devised a way to run deep neural networks on mobile devices like the average cell phone. Photo: Ruby Wallau/Northeastern University |
The answer is a type of artificial intelligence known as deep neural networks. These networks are very good at recognizing and classifying data, but they tend to take a lot of computing power and memory to run—too much to work quickly on something like your average smartphone.
Now researchers at Northeastern have demonstrated a way to run deep neural networks on a smartphone or similar system. Using their method, the networks can execute tasks up to 56 times faster than demonstrated in previous work, without losing accuracy. They will be presenting their work at a conference on artificial intelligence next month in New York.
"It is difficult for people to achieve the real-time execution of neural networks on a smartphone or these kinds of mobile devices," says Yanzhi Wang, an assistant professor of electrical and computer engineering at Northeastern. "But we can make most deep learning applications work in real-time."...
Wang and his colleagues have devised a way to both reduce the size of the neural network model and automatically generate code to run it more efficiently. This work could allow deep neural networks to be implemented in off-the-shelf devices that may not have consistent internet access. And that has uses far beyond hands-free communication with your phone.
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Additional resources
PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices. arXiv:1909.05073v3 [cs.LG]:
arxiv.org/abs/1909.05073
Source: Tech Xplore
