Audio Classification with Skyrmion Reservoirs
arXiv:2209.13946 · doi:10.1002/aisy.202200388
Abstract
Physical reservoir computing is a computational paradigm that enables spatio-temporal pattern recognition to be performed directly in matter. The use of physical matter leads the way towards energy-efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. We propose a high performance "skyrmion mixture reservoir" that implements the reservoir computing model with multi-dimensional inputs. We show that our implementation solves spoken digit classification tasks at the nanosecond timescale, with an overall model accuracy of 97.4% and a less that 1% word error rate; the best performance ever reported for in-materio reservoir computers. Due to the quality of the results and the low power properties of magnetic texture reservoirs, we argue that skyrmion fabrics are a compelling candidate for reservoir computing.
arXiv comment6 pages, 4 figures
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Cited by in corpus (7)
- Perspective on unconventional computing using magnetic skyrmions
- Handwritten Digit Recognition by Spin Waves in a Skyrmion Reservoir
- Circular motion of non-collinear spin textures in Corbino disks: Dynamics of Néel- versus Bloch-type skyrmions and skyrmioniums
- In-Materia Speech Recognition
- RC circuit based on magnetic skyrmions
- Metrics for spin-based computing
- Correlating on-the-fly Electrical and Optical Skyrmion Readout