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Saptarshi Das
Pennsylvania State University
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ORCiD: https://orcid.org/0000-0002-0188-945X
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In spite of recent advancements in bio-realistic artificial neural networks such as spiking neural networks (SNNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of biological neural networks (BNNs) largely remain unimitated in hardware neuromorphic computing systems. Here we exploit optoelectronic and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS2 to demonstrate an “all-in-one” hardware SNN system which is capable of sensing, encoding, unsupervised learning, and inference at miniscule energy expenditure. In short, we have utilized photogating effect in MoS2 based neuromorphic phototransistor for sensing and direct encoding of analog optical information into graded spike trains, we have designed MoS2 based neuromorphic encoding module for conversion of spike trains into spike-count and spike-timing based programming voltages, and finally we have used arrays of programmable MoS2 non-volatile synapses for spike-based unsupervised learning and inference. We also demonstrate adaptability of our SNN for learning under scotopic (low-light) and photopic (bright-light) conditions mimicking neuroplasticity of BNNs. Furthermore, we use our hardware SNN platform to show learning challenges under specific synaptic conditions, which can aid in understanding learning disabilities in BNNs. Our findings highlight the potential of in-memory computing and sensing based on emerging 2D materials, devices, and circuits not only to overcome the bottleneck of von Neumann computing in conventional CMOS designs but also aid in eliminating peripheral components necessary for competing technologies such as memristors, RRAM, PCM, etc. as well as bridge the understanding between neuroscience of learning and machine learning.
Keywords
spiking neural networks (SNNs), artificial neural networks, biological neural networks (BNNs)
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Supplementary_Video_3
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Supplementary_Video_4
- SupplementaryVideo5.mp4
Supplementary_Video_5
- SupplementaryVideo6.mp4
Supplementary_Video_6
- SupplementaryInformationNM.pdf
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A new preprint design is coming!
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This is a preprint, 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.
Article
Saptarshi Das
Pennsylvania State University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0188-945X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 19 Feb, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In spite of recent advancements in bio-realistic artificial neural networks such as spiking neural networks (SNNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of biological neural networks (BNNs) largely remain unimitated in hardware neuromorphic computing systems. Here we exploit optoelectronic and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS2 to demonstrate an “all-in-one” hardware SNN system which is capable of sensing, encoding, unsupervised learning, and inference at miniscule energy expenditure. In short, we have utilized photogating effect in MoS2 based neuromorphic phototransistor for sensing and direct encoding of analog optical information into graded spike trains, we have designed MoS2 based neuromorphic encoding module for conversion of spike trains into spike-count and spike-timing based programming voltages, and finally we have used arrays of programmable MoS2 non-volatile synapses for spike-based unsupervised learning and inference. We also demonstrate adaptability of our SNN for learning under scotopic (low-light) and photopic (bright-light) conditions mimicking neuroplasticity of BNNs. Furthermore, we use our hardware SNN platform to show learning challenges under specific synaptic conditions, which can aid in understanding learning disabilities in BNNs. Our findings highlight the potential of in-memory computing and sensing based on emerging 2D materials, devices, and circuits not only to overcome the bottleneck of von Neumann computing in conventional CMOS designs but also aid in eliminating peripheral components necessary for competing technologies such as memristors, RRAM, PCM, etc. as well as bridge the understanding between neuroscience of learning and machine learning.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
The full text of this article is available to read as a PDF.
There is NO Competing Interest.
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryVideo1.mp4
Supplementary_Video_1
- SupplementaryVideo2.mp4
Supplementary_Video_2
- SupplementaryVideo3.mp4
Supplementary_Video_3
- SupplementaryVideo4.mp4
Supplementary_Video_4
- SupplementaryVideo5.mp4
Supplementary_Video_5
- SupplementaryVideo6.mp4
Supplementary_Video_6
- SupplementaryInformationNM.pdf
Supplementary Information
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