# Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics

arXiv: https://arxiv.org/abs/2111.14603 · HTML: https://mlsanity.com/paper/2111.14603/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 59 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate](https://mlsanity.com/paper/1912.12980/) (2019)
- [Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing](https://mlsanity.com/paper/2004.14942/) (2020)
- [Role of non-linear data processing on speech recognition task in the framework of reservoir computing](https://mlsanity.com/paper/1906.02812/) (2019)
- [SpaRCe: Improved Learning of Reservoir Computing Systems through Sparse Representations](https://mlsanity.com/paper/1912.08124/) (2019)
- [Protein Structured Reservoir computing for Spike-based Pattern Recognition](https://mlsanity.com/paper/2008.03330/) (2020)

## Built on by (most cited first, 4 of 4)

- [A perspective on physical reservoir computing with nanomagnetic devices](https://mlsanity.com/paper/2212.04851/) (2022)
- [Noise-Aware Training of Neuromorphic Dynamic Device Networks](https://mlsanity.com/paper/2401.07387/) (2024)
- [Reservoir Computing Benchmarks: a tutorial review and critique](https://mlsanity.com/paper/2405.06561/) (2024)
- [Machine learning using magnetic stochastic synapses](https://mlsanity.com/paper/2303.01886/) (2023)

## Abstract (arXiv)

Devices based on arrays of interconnected magnetic nano-rings with emergent magnetization dynamics have recently been proposed for use in reservoir computing applications, but for them to be computationally useful it must be possible to optimise their dynamical responses. Here, we use a phenomenological model to demonstrate that such reservoirs can be optimised for classification tasks by tuning hyperparameters that control the scaling and input rate of data into the system using rotating magnetic fields. We use task-independent metrics to assess the rings' computational capabilities at each set of these hyperparameters and show how these metrics correlate directly to performance in spoken and written digit recognition tasks. We then show that these metrics, and performance in tasks, can be further improved by expanding the reservoir's output to include multiple, concurrent measures of the ring arrays magnetic states.