Non-linear processing with a Surface Acoustic Wave reservoir computer

Meffan, Robert Claude; Ijima, Taiki; Banerjee, Amit; Hirotani, Jun; Tsuchiya, Toshiyuki

doi:10.21203/rs.3.rs-2430313/v1

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Another promising approach to physical reservoir computing employs acoustic waves, vibrations and adjacent physical processes [105][106][107]. Although high-frequency (MHz-range) acoustic waves have been used in the cited papers, the ideas presented in those works can be implemented using the acoustic phenomena observed in a wide range of frequencies.…”

Section: Acoustic-based Reservoir Computingmentioning

confidence: 99%

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Abbas,

Abdel-Ghani,

Maksymov

2024

Preprint

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Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of total power available onboard, thereby limiting the vehicle’s range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using the traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives of development of onboard neuromorphic computers that mimic the operation of a biological brain using nonlinear-dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs is a semi-classical technology, their technical simplicity and low, compared with quantum computers, cost make them ideally suitable for application in autonomous AI system. Providing a perspective view on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.

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Section: Acoustic-based Reservoir Computingmentioning

confidence: 99%

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Abbas,

Abdel-Ghani,

Maksymov

2024

Preprint

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“…One of the first realisations was based on waves, namely gravity waves in a vessel with water to recognize spoken numbers [10]. Nowadays, wave 'reservoirs' have been implemented using different physical waves and their quanta such as photons [11][12][13][14][15], phonons [16], and magnons [17][18][19]. The key factors that make waves reliable for reservoir architectures are the possibility to achieve wave packets with a vast information density; weighted summation by interference; nonlinearity resulting in the wave mixing and generation of higher harmonics; hybridization of waves of different types, e.g., the formation of polarons.…”

Section: Introductionmentioning

confidence: 99%

On-chip phonon-magnon reservoir for neuromorphic computing

Scherbakov

Yaremkevich

Clerk

et al. 2023

Preprint

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Reservoir computing is a concept in which signals to process are mapped onto a high-dimensional phase space of a fixed dynamical system called “reservoir” for subsequent recognition by an artificial neural network. Implementations of reservoirs are possible using different hardware and, accordingly, exploit different carriers and mechanisms of signal transformation. Despite the growing number of neuromorphic prototypes of reservoirs, demands for miniaturization, efficiency, and robustness all require implementation of the reservoir on a chip, and remain among the key challenges. Here we propose a nanodevice, in which a sandwich of a semiconductor phonon waveguide and a patterned ferromagnetic layer enables efficient reservoir computing. The optical input signal is coded by a pulsed write-laser and converted into a propagating multimode phonon wave packet, which interacts with a bunch of magnon modes. The output signal read by a second laser represents a phase-sensitive superposition of all the phonon and magnon modes, which possesses ultimate sensitivity to the relative positions of the write- and read-laser spots. The reservoir efficiently separates the visual shapes drawn by the write-laser beam on the nanodevice surface in an area with size comparable to a single pixel of a modern digital camera. Thus, our finding suggests the phonon-magnon interaction as a promising hardware basis for realization of rapid on-chip reservoir computing for future neuromorphic architectures.

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Non-linear processing with a Surface Acoustic Wave reservoir computer

Cited by 2 publications

References 19 publications

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

On-chip phonon-magnon reservoir for neuromorphic computing

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