Scaling for edge inference of deep neural networks
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2018/04/17
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Journal
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Indian UGC (journal)
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Refrences120
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Citations202
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Xiaowei Xu ORCID (unauthenticated)
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Yukun Ding
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Sharon Xiaobo Hu
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Michael Niemier
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Jason Cong
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Yu Hu
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Yiyu Shi
Cite
Xu, Xiaowei, et al. “Scaling for Edge Inference of Deep Neural Networks”. Nature Electronics, vol. 1, no. 4, 2018, pp. 216-22, https://doi.org/10.1038/s41928-018-0059-3.
Xu, X., Ding, Y., Hu, S. X., Niemier, M., Cong, J., Hu, Y., & Shi, Y. (2018). Scaling for edge inference of deep neural networks. Nature Electronics, 1(4), 216-222. https://doi.org/10.1038/s41928-018-0059-3
Xu, Xiaowei, Yukun Ding, Sharon Xiaobo Hu, Michael Niemier, Jason Cong, Yu Hu, and Yiyu Shi. “Scaling for Edge Inference of Deep Neural Networks”. Nature Electronics 1, no. 4 (2018): 216-22. https://doi.org/10.1038/s41928-018-0059-3.
Xu X, Ding Y, Hu SX, Niemier M, Cong J, Hu Y, et al. Scaling for edge inference of deep neural networks. Nature Electronics. 2018;1(4):216-22.
Journal Categories
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Analogue signal and image processing with large memristor crossbars | Nature Electronics |
| 746 | 2018 |
| 10.1109/JPROC.2017.2761740 | Proceedings of the IEEE |
| 2017 | |
| Error bounds for approximations with deep ReLU networks | Neural Networks |
| 424 | 2017 |
| Quantitative analysis of diffusion-weighted magnetic resonance images: differentiation between prostate cancer and normal tissue based on a computer-aided diagnosis system | Science China Life Sciences |
| 12 | 2017 |
| 10.1109/TMAG.2018.2792846 | 2017 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Resistive Memory Devices at the Thinnest Limit: Progress and Challenges | Advanced Materials |
| 2 | 2024 |
Ultrathin All‐Solid‐State MoS2‐Based Electrolyte Gated Synaptic Transistor with Tunable Organic–Inorganic Hybrid Film | Advanced Science |
| 2024 | |
| Diffusion-based Wasserstein generative adversarial network for blood cell image augmentation | Engineering Applications of Artificial Intelligence |
| 2024 | |
| Chalcogen Vacancy Engineering of Two-Dimensional Transition Metal Dichalcogenides for Electronic Applications | ACS Applied Nano Materials |
| 2024 | |
| Role of all-optical neural networks | Physical Review Applied |
| 2024 |
Citations Analysis
The category
Technology: Chemical technology 65
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Efficient Hardware Implementation of Cellular Neural Networks with Incremental Quantization and Early Exit and was published in 2018. The most recent citation comes from a 2024 study titled Ultrathin All‐Solid‐State MoS2‐Based Electrolyte Gated Synaptic Transistor with Tunable Organic–Inorganic Hybrid Film. This article reached its peak citation in 2022, with 57 citations. It has been cited in 107 different journals, 23% of which are open access. Among related journals, the Nature Electronics cited this research the most, with 11 citations. The chart below illustrates the annual citation trends for this article.