Experimentally realized memristive memory augmented neural network Article Swipe
Ruibin Mao
,
Bo Wen
,
Arman Kazemi
,
Yahui Zhao
,
Ann Franchesca Laguna
,
Rui Lin
,
Ngai Wong
,
Michael Niemier
,
Xiaobo Sharon Hu
,
Xia Sheng
,
Catherine E. Graves
,
John Paul Strachan
,
Can Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1821052/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1821052/v1
Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory augmented neural network has been proposed to achieve the goal, but the memory module has to be stored in an off-chip memory due to its size. Therefore its practical use has been heavily limited. Previous works on emerging memory-based implementation had difficulties in scaling up because different modules with various structures were difficult to integrate on the same chip and the small sense margin of the content addressable memory for the memory module heavily limited the degree of mismatch calculation. In this work, we experimentally validated that all those different structures in the memory augmented neural network architecture can be implemented in a fully integrated memristive crossbar platform and achieve an accuracy that closely matches standard software on digital hardware for the Omniglot dataset. The successful demonstration is supported by implementing new functions in crossbars in addition to widely reported matrix multiplications. For example, the locality-sensitive hashing operation is implemented in crossbar arrays by exploiting the intrinsic stochasticity of memristor devices. Besides, the content-addressable memory module is realized in crossbars, which also supports the degree of mismatches. Simulations based on experimentally validated models show such an implementation can be efficiently scaled up for one-shot learning on the Mini-ImageNet dataset. The successful demonstration paves the way for practical on-device lifelong learning and opens possibilities for novel attention-based algorithms not possible in conventional hardware.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1821052/v1
- https://www.researchsquare.com/article/rs-1821052/latest.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4223892805
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- OpenAlex ID
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https://openalex.org/W4223892805Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-1821052/v1Digital Object Identifier
- Title
-
Experimentally realized memristive memory augmented neural networkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-05Full publication date if available
- Authors
-
Ruibin Mao, Bo Wen, Arman Kazemi, Yahui Zhao, Ann Franchesca Laguna, Rui Lin, Ngai Wong, Michael Niemier, Xiaobo Sharon Hu, Xia Sheng, Catherine E. Graves, John Paul Strachan, Can LiList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-1821052/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-1821052/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-1821052/latest.pdfDirect OA link when available
- Concepts
-
Artificial neural network, Memristor, Computer science, Artificial intelligence, Electronic engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2022: 1Per-year citation counts (last 5 years)
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53Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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