Phys.org September 16, 2024 Magnetic skyrmions are promising candidates for reservoir computing systems due to their enhanced stability, non-linear interactions and low-power manipulation. Traditional spin-based reservoir computing has been limited to quasi-static detection or real-world data must be rescaled to the intrinsic timescale of the reservoir. An international team of researchers (Germany, The Netherlands, Norway) addressed this challenge by time-multiplexed skyrmion reservoir computing, that allowed for aligning the reservoir’s intrinsic timescales to real-world temporal patterns. Using millisecond-scale hand gestures recorded with Range-Doppler radar, they fed voltage excitations directly into their device and detected the skyrmion trajectory evolution. This method was […]
Tag Archives: Reservoir computing
New possibilities for reservoir computing with topological magnetic and ferroelectric systems
Phys.org July 3, 2024 Topological spin textures in magnetic materials and arrangements of electric dipoles in ferroelectrics are promising candidates for next-generation information technology and unconventional computing. Exciting examples are magnetic skyrmions and ferroelectric domain walls. In their article an international team of researchers (Germany, Norway) discussed how the physical properties of these topological nanoscale systems can be leveraged for reservoir computing, that is, for translating non-linear problems into linearly solvable ones. Topological nanoscale systems fulfill the requirements for non-linearity, complexity, short-term memory and reproducibility, giving new opportunities for the downscaling of devices, enhanced complexity and versatile input and readout […]
Listen up, material!
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 […]
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 […]