Implementation of DNNs on IoT devices
Article Properties
-
LanguageEnglish
-
DOI (url)
-
Publication Date2019/10/17
-
Indian UGC (journal)
-
Refrences147
-
Citations13
-
Zhichao Zhang
-
Abbas Z. Kouzani
Cite
Zhang, Zhichao, and Abbas Z. Kouzani. “Implementation of DNNs on IoT Devices”. Neural Computing and Applications, vol. 32, no. 5, 2019, pp. 1327-56, https://doi.org/10.1007/s00521-019-04550-w.
Zhang, Z., & Kouzani, A. Z. (2019). Implementation of DNNs on IoT devices. Neural Computing and Applications, 32(5), 1327-1356. https://doi.org/10.1007/s00521-019-04550-w
Zhang, Zhichao, and Abbas Z. Kouzani. “Implementation of DNNs on IoT Devices”. Neural Computing and Applications 32, no. 5 (2019): 1327-56. https://doi.org/10.1007/s00521-019-04550-w.
Zhang Z, Kouzani AZ. Implementation of DNNs on IoT devices. Neural Computing and Applications. 2019;32(5):1327-56.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Technology
Engineering (General)
Civil engineering (General)
Technology
Mechanical engineering and machinery
You May Also Like
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| AI for 5G: research directions and paradigms | Science China Information Sciences |
| 80 | 2019 |
FPGA-Based Hybrid-Type Implementation of Quantized Neural Networks for Remote Sensing Applications | Sensors |
| 21 | 2019 |
| 10.1109/TII.2019.2902878 | 2019 | |||
FPGA Dynamic and Partial Reconfiguration | ACM Computing Surveys |
| 65 | 2018 |
Toolflows for Mapping Convolutional Neural Networks on FPGAs | ACM Computing Surveys |
| 62 | 2018 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| IR$$^2$$Net: information restriction and information recovery for accurate binary neural networks | Neural Computing and Applications |
| 2023 | |
Hierarchical multi-scale parametric optimization of deep neural networks | Applied Intelligence |
| 2023 | |
| Cellular Internet of Things: Use cases, technologies, and future work | Internet of Things | 4 | 2023 | |
| Trusted Deep Neural Execution—A Survey | IEEE Access |
| 2023 | |
| Decentralized Configuration of TSCH-Based IoT Networks for Distinctive QoS: A Deep Reinforcement Learning Approach | IEEE Internet of Things Journal | 2023 |
Citations Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics 8
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Resource-constrained FPGA/DNN co-design and was published in 2021. The most recent citation comes from a 2023 study titled Cellular Internet of Things: Use cases, technologies, and future work. This article reached its peak citation in 2022, with 6 citations. It has been cited in 10 different journals, 20% of which are open access. Among related journals, the Neural Computing and Applications cited this research the most, with 3 citations. The chart below illustrates the annual citation trends for this article.