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Article
Hirofumi Tanaka
Kyushu Institute of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-4378-5747
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This work is licensed under a CC BY 4.0 License. Read Full License
Modern applications of artificial intelligence (AI) are generally algorithmic in nature and implemented using either general-purpose or application-specific hardware systems that have high power requirements. In the present study, physical (in-materio) reservoir computing (RC) implemented in hardware was explored as an alternative to software-based AI. The device, made up of a random, highly interconnected network of nonlinear Ag2Se nanojunctions, demonstrated the requisite characteristics of an in-materio reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. As a hardware reservoir, the devices successfully performed waveform generation tasks, where tasks conducted at elevated network temperatures were found to be more stable than those conducted at room temperature. Finally, a comparison of voice classification, with and without the network device, showed that classification performance increased in the presence of the network device.
Keywords
Artificial Intelligence, Nonlinear Nanojunctions, Higher Harmonic Generation, Waveform Generation Tasks, Voice Classification
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This preprint is under consideration at a Nature Portfolio Journal. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Nature journals have partnered with Research Square to offer In Review, a journal-integrated preprint deposition service.
Article
Hirofumi Tanaka
Kyushu Institute of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-4378-5747
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 17 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Modern applications of artificial intelligence (AI) are generally algorithmic in nature and implemented using either general-purpose or application-specific hardware systems that have high power requirements. In the present study, physical (in-materio) reservoir computing (RC) implemented in hardware was explored as an alternative to software-based AI. The device, made up of a random, highly interconnected network of nonlinear Ag2Se nanojunctions, demonstrated the requisite characteristics of an in-materio reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. As a hardware reservoir, the devices successfully performed waveform generation tasks, where tasks conducted at elevated network temperatures were found to be more stable than those conducted at room temperature. Finally, a comparison of voice classification, with and without the network device, showed that classification performance increased in the presence of the network device.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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