@inproceedings{
jong2023virtual,
title={Virtual reservoir acceleration for {CPU} and {GPU}: Case study for coupled spin-torque oscillator reservoir},
author={Thomas De Jong and Nozomi Akashi and Tomohiro Taniguchi and Hirofumi Notsu and Kohei Nakajima},
booktitle={Machine Learning with New Compute Paradigms},
year={2023},
url={https://openreview.net/forum?id=SL8VJMjOHU}
}
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 $10^4$. More specifically,  over all implementations the best factor is 78.9 for $N=1$ which  decreases to 2.6 for $N=10^3$ and finally increases to 23.8 for $N=10^4$.  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.
Virtual reservoir acceleration for CPU and GPU: Case study for coupled spin-torque oscillator reservoir
Thomas De Jong Nozomi Akashi Tomohiro Taniguchi Hirofumi Notsu Kohei Nakajima