Nanowerk March 27, 2023 Physical reservoir computing is a computational paradigm that enables spatiotemporal pattern recognition to be performed directly in matter. The use of physical matter leads the way toward energy-efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. An international team of researchers (Germany, Belgium) proposed a high-performance “skyrmion mixture reservoir” that implemented the reservoir computing model with multidimensional inputs. This implementation solved spoken digit classification tasks with an overall model accuracy of 97.4% and a < 1% word error rate. According to the researchers due to the quality of […]
Tag Archives: Reservoir computing
Scientists develop the next generation of reservoir computing
Phys.org September 21, 2021 Reservoir computing is a machine learning algorithm developed in the early 2000s and used to solve the “hardest of the hard” computing problems. It requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. It does that using an artificial neural network which is a black box. A team of researchers in the US (Ohio State University, industry, Clackson University) investigated the “black box” and found that the whole reservoir computing system could be greatly simplified, dramatically reducing the need for computing resources and saving significant time. They tested their concept […]