A Flexible Implementation Model for Neural Networks on FPGAs-Reference-Cited by-同舟云学术

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A Flexible Implementation Model for Neural Networks on FPGAs

Published:2023 Issue: Volume: Page:332-342
ISSN:2367-3370
Container-title:Intelligent Systems Design and Applications
language:
Short-container-title:

Author:

Jakobsen Jesper,Jensen Mikkel,Sharifirad Iman,Boudjadar Jalil

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-35501-1_33

Reference25 articles.

1. Yamashita, T., Tanaka, M., Yamauchi, Y., Fujiyoshi, H.: SWAP-NODE: a regularization approach for deep convolutional neural networks. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 2475–2479 (2015)

2. Boudjadar, J., Kim, J.H., Nadjm-Tehrani, S.: Performance-aware scheduling of multicore time-critical systems. In: 2016 ACM/IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE), pp. 105–114 (2016)

3. Semmad, A., Bahoura, M.: Serial hardware architecture of multilayer neural network for automatic wheezing detection. In: International Midwest Symposium on Circuits and Systems (2021)

4. Morcos, B., Stewart, T.C., Eliasmith, C., Kapre, N.: Implementing NEF neural networks on embedded FPGAs. In: International Conference on Field-Programmable Technology (FPT), pp. 22–29 (2018)

5. Bahoura, M., Park, C.-W.: FPGA-implementation of an adaptive neural network for RF power amplifier modeling. In: 2011 IEEE 9th International New Circuits and Systems Conference, pp. 29–32 (2011)

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1. Advanced Acceleration and Implementation of Convolutional Neural Networks on FPGAs;2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2023-12-17

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