Scientists have vastly accelerated the speed and efficiency of reservoir computing to tackle some of the most difficult information processing challenges.
An Ohio State University (OSU) team used next-generation reservoir computing on a Lorenz weather prediction task, which was 33 to 163 times faster than the current-generation model.
The new model also was about a million times faster in terms of forecasting accuracy, and OSU's Daniel Gauthier said it achieved this improvement using the equivalent of just 28 neurons, versus 4,000 for the current-generation model.
Gauthier credited the speedup to the new model's reduced warmup and training time, adding, "What's exciting is that this next generation of reservoir computing takes what was already very good and makes it significantly more efficient."
From Ohio State News
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