# A perspective on physical reservoir computing with nanomagnetic devices

arXiv: https://arxiv.org/abs/2212.04851 · HTML: https://mlsanity.com/paper/2212.04851/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 79 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Vowel recognition with four coupled spin-torque nano-oscillators](https://mlsanity.com/paper/1711.02704/) (2017)
- [Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations](https://mlsanity.com/paper/2108.02318/) (2021)
- [Exploiting Multiple Timescales in Hierarchical Echo State Networks](https://mlsanity.com/paper/2101.04223/) (2021)
- [SpaRCe: Improved Learning of Reservoir Computing Systems through Sparse Representations](https://mlsanity.com/paper/1912.08124/) (2019)
- [Quantifying the Computational Capability of a Nanomagnetic Reservoir Computing Platform with Emergent Magnetization Dynamics](https://mlsanity.com/paper/2111.14603/) (2021)
- [EchoVPR: Echo State Networks for Visual Place Recognition](https://mlsanity.com/paper/2110.05572/) (2021)
- [Reservoir Computing with Planar Nanomagnet Arrays](https://mlsanity.com/paper/2003.10948/) (2020)

## Built on by (most cited first, 12 of 12)

- [Analogue and Physical Reservoir Computing Using Water Waves](https://mlsanity.com/paper/2306.09095/) (2023)
- [Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective](https://mlsanity.com/paper/2407.04717/) (2024)
- [Reservoir Computing Using Measurement-Controlled Quantum Dynamics](https://mlsanity.com/paper/2403.01024/) (2024)
- [The Physics of Preference: Unravelling Imprecision of Human Preferences through Magnetisation Dynamics](https://mlsanity.com/paper/2310.00267/) (2023)
- [Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime](https://mlsanity.com/paper/2504.07221/) (2025)
- [Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting](https://mlsanity.com/paper/2304.03343/) (2023)
- [Mixed Delay/Nondelay Embeddings Based Neuromorphic Computing with Patterned Nanomagnet Arrays](https://mlsanity.com/paper/2412.04622/) (2024)
- [Learning Nonlinear Heterogeneity in Physical Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2601.15340/) (2026)
- [Machine learning using magnetic stochastic synapses](https://mlsanity.com/paper/2303.01886/) (2023)
- [OpenPRC: A Unified Open-Source Framework for Physics-to-Task Evaluation in Physical Reservoir Computing](https://mlsanity.com/paper/2604.07423/) (2026)
- [HiRo: A Compact Four-Directional Hierarchical Reservoir Token-Mixer for Efficient Image Classification](https://mlsanity.com/paper/2606.15151/) (2026)
- [Low-power analogue neural networks with trainable nonlinear connections for continuous control](https://mlsanity.com/paper/2606.23742/) (2026)

## Abstract (arXiv)

Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.