Neural Networks With Multiplexing

pen-helm writes:

https://spectrum.ieee.org/neural-network-multiplex

Researchers find neural networks can process a lot more data - achieving up to an 18-fold speedup - by multiplexing many inputs into one feed. They don't yet know why this doesn't confuse the network.

Just as multiplexing can help a single communication channel carry many signals at the same time, a new study reveals that multiplexing can help neural networks-the AI systems that now often power speech recognition, computer vision, and more-scan dozens of streams of data simultaneously, letting them greatly boost the rate at which they analyze information.

In artificial neural networks, components dubbed "neurons" are fed data and cooperate to solve a problem, such as recognizing images. The neural net repeatedly adjusts the links between its neurons and sees if the resulting patterns of behavior are better at finding a solution. Over time, the network discovers which patterns are best at computing results. It then adopts these as defaults, mimicking the process of learning in the human brain. The features of a neural net that change with learning, such as the nature of the connections between neurons, are known as its parameters.

Recent research suggests that modern neural networks often have vastly more parameters than they need-potentially, they could prune the numbers of their parameters by more than 90 percent to reduce their sizes without harming their accuracy. This raised a question that researchers at Princeton University aimed to address-if neural networks possessed more computing power than they needed, could they each analyze multiple streams of information simultaneously to help learn a task, just as a radio channel can share its bandwidth to carry multiple signals at the same time?

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