Virtual reservoir acceleration for CPU and GPU: Case study for coupled spin-torque oscillator reservoir
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Abstract (arXiv)
We provide high-speed implementations for simulating reservoirs described by N-coupled spin-torque oscillators. Here N also corresponds to the number of reservoir nodes. We benchmark a variety of implementations based on CPU and GPU. Our new methods are at least 2.6 times quicker than the baseline for N in range 1 to 104. More specifically, over all implementations the best factor is 78.9 for N=1 which decreases to 2.6 for N=103 and finally increases to 23.8 for N=104. GPU outperforms CPU significantly at N=2500. Our results show that GPU implementations should be tested for reservoir simulations. The implementations considered here can be used for any reservoir with evolution that can be approximated using an explicit method.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 43 references that are in the map. First posted on arXiv 2 Dec 2023.
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References · 10
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
- 2019Hamiltonian Neural Networkscited by 702
- 2020Lagrangian Neural Networkscited by 388
- 2020The Computational Limits of Deep Learningcited by 188
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- 2020Physical reservoir computing -- An introductory perspectivecited by 58
- 2017Vowel recognition with four coupled spin-torque nano-oscillatorscited by 22
- 2020Multifunctionality in a Reservoir Computercited by 21
- 2019A Unifying Framework for Information Processing in Stochastically Driven Dynamical Systemscited by 16
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