Deep random forest with ferroelectric analog content addressable memory Article Swipe
Xunzhao Yin
,
Franz Mȕller
,
Ann Franchesca Laguna
,
Chao Li
,
Qingrong Huang
,
Zhiguo Shi
,
Maximilian Lederer
,
Nellie Laleni
,
Shan Deng
,
Zijian Zhao
,
Mohsen Imani
,
Yiyu Shi
,
Michael Niemier
,
Xiaobo Sharon Hu
,
Cheng Zhuo
,
Thomas Kämpfe
,
Kai Ni
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1126/sciadv.adk8471
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1126/sciadv.adk8471
Deep random forest (DRF), which combines deep learning and random forest, exhibits comparable accuracy, interpretability, low memory and computational overhead to deep neural networks (DNNs) in edge intelligence tasks. However, efficient DRF accelerator is lagging behind its DNN counterparts. The key to DRF acceleration lies in realizing the branch-split operation at decision nodes. In this work, we propose implementing DRF through associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell performs energy-efficient branch-split operations by storing decision boundaries as analog polarization states in FeFETs. The DRF accelerator architecture and its model mapping to ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM DRF and its robustness against FeFET device non-idealities are validated in experiments and simulations. Evaluations show that the FeFET ACAM DRF accelerator achieves ∼10 6 ×/10× and ∼10 6 ×/2.5× improvements in energy and latency, respectively, compared to other DRF hardware implementations on state-of-the-art CPU/ReRAM.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1126/sciadv.adk8471
- OA Status
- gold
- Cited By
- 21
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399376491
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4399376491Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1126/sciadv.adk8471Digital Object Identifier
- Title
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Deep random forest with ferroelectric analog content addressable memoryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-05Full publication date if available
- Authors
-
Xunzhao Yin, Franz Mȕller, Ann Franchesca Laguna, Chao Li, Qingrong Huang, Zhiguo Shi, Maximilian Lederer, Nellie Laleni, Shan Deng, Zijian Zhao, Mohsen Imani, Yiyu Shi, Michael Niemier, Xiaobo Sharon Hu, Cheng Zhuo, Thomas Kämpfe, Kai NiList of authors in order
- Landing page
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https://doi.org/10.1126/sciadv.adk8471Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1126/sciadv.adk8471Direct OA link when available
- Concepts
-
Ferroelectricity, Random forest, Computer science, Content (measure theory), Artificial intelligence, Materials science, Optoelectronics, Mathematics, Mathematical analysis, DielectricTop concepts (fields/topics) attached by OpenAlex
- Cited by
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21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 14, 2024: 6, 2023: 1Per-year citation counts (last 5 years)
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59Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| countries_distinct_count | 4 |
| institutions_distinct_count | 17 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.99568966 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |