A new way to solve the ‘hardest of the hard’ computer problems

A relatively new type of computing that mimics the way the human brain works was already transforming how scientists could tackle some of the most difficult information processing problems.

Now, researchers have found a way to make what is called reservoir computing work between 33 and a million times faster, with significantly fewer computing resources and less data input needed.

In fact, in one test of this next-generation reservoir computing, researchers solved a complex computing problem in less than a second on a desktop computer.

Daniel Gauthier

Using the now current state-of-the-art technology, the same problem requires a supercomputer to solve and still takes much longer, said Prof. Daniel Gauthier, lead author of the study and professor of physics at The Ohio State University.

“We can perform very complex information processing tasks in a fraction of the time using much less computer resources compared to what reservoir computing can currently do,” Gauthier said.

“And reservoir computing was already a significant improvement on what was previously possible.”

The study was published today (Sept. 21, 2021) in the journal Nature Communications.

Reservoir computing is a machine learning algorithm developed in the early 2000s and used to solve the “hardest of the hard” computing problems, such as forecasting the evolution of dynamical systems that change over time, Gauthier said.

Dynamical systems, like the weather, are difficult to predict because just one small change in one condition can have massive effects down the line, he said.

One famous example is the “butterfly effect,” in which – in one metaphorical example – changes created by a butterfly flapping its wings can eventually influence the weather weeks later.

Previous research has shown that reservoir computing is well-suited for learning dynamical systems and can provide accurate forecasts about how they will behave in the future, Gauthier said.

It does that through the use of an artificial neural network, somewhat like a human brain. Scientists feed data on a dynamical network into a “reservoir” of randomly connected artificial neurons in a network. The network produces useful output that the scientists can interpret and feed back into the network, building a more and more accurate forecast of how the system will evolve in the future.

Article by Jeff Grabmeier, Ohio State News

Caption: Artificial neural networks - the heart of reservoir computing - have been greatly simplified.