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Hyungwoo Lee
Ajou University
Corresponding Author
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Memristors are essential elements for hardware implementation of artificial neural networks. The key functionality of the memristors is to realize multiple non-volatile conductance states with high precision. However, the variation of device conductance limits the number of allowed states. Since actual data for neural network training inherently have a non-uniform distribution, the insufficient number of conductance states and the resultant inaccurate weight quantization may generate significant errors in the memristor-based computation. Herein, we demonstrate a multi-level memristor based on two-dimensional electron gas in a Pt/LaAlO3/SrTiO3 heterostructure. By redistributing oxygen vacancies, we precisely controlled the tunneling conductance of the device, achieving multiple conductance states (more than 27). The multi-level switching capability and the high retention performance allow us to implement a variance-aware weight quantization (VAQ), designed for improved computing accuracy. We verify that the VAQ provides greater accuracy in image classification process, as compared to conventional uniform quantization. These results provide valuable insight into developing high-precision multi-bit memristors for practical neuromorphic processors.
Keywords
oxygen vacancies, tunneling conductance, variance-aware weight quantization
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- 2DEGMemristorSupplementary.docx
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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
Hyungwoo Lee
Ajou University
Corresponding Author
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 11 Nov, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Memristors are essential elements for hardware implementation of artificial neural networks. The key functionality of the memristors is to realize multiple non-volatile conductance states with high precision. However, the variation of device conductance limits the number of allowed states. Since actual data for neural network training inherently have a non-uniform distribution, the insufficient number of conductance states and the resultant inaccurate weight quantization may generate significant errors in the memristor-based computation. Herein, we demonstrate a multi-level memristor based on two-dimensional electron gas in a Pt/LaAlO3/SrTiO3 heterostructure. By redistributing oxygen vacancies, we precisely controlled the tunneling conductance of the device, achieving multiple conductance states (more than 27). The multi-level switching capability and the high retention performance allow us to implement a variance-aware weight quantization (VAQ), designed for improved computing accuracy. We verify that the VAQ provides greater accuracy in image classification process, as compared to conventional uniform quantization. These results provide valuable insight into developing high-precision multi-bit memristors for practical neuromorphic processors.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
There is NO Competing Interest.
This is a list of supplementary files associated with this preprint. Click to download.
- 2DEGMemristorSupplementary.docx
Supplementary Information
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