FPGA Implementation of Efficient Softmax Architecture for Deep Neural Networks
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6855-8_47
Reference10 articles.
1. Cardarilli GC et al (2021) A pseudo-softmax function for hardwarebased high-speed image classification. Sci Rep 11. Article ID: 15307
2. Wei Z, Arora A, Patel P, John L (2020) Design space exploration for softmax implementations. In: Proceedings of 31st IEEE International conference on application-specific systems, architectures and processors (ASAP), pp 45–52
3. Yuan B (2016) Efficient hardware architecture of softmax layer in deep neural network. In: Proceedings of 29th IEEE International system-on-chip conference (SOCC), pp 323–326
4. Du G, Tian C, Li Z, Zhang D, Yin Y-S, Ouyang Y (2019) Efficient softmax hardware architecture for deep neural networks. In: Proceedings of great lakes symposium VLSI (GLSVLSI), pp 75–80
5. Spagnolo F, Perri S, Corsonello P (2022) Aggressive approximation of the SoftMax function for power-efficient hardware implementations. IEEE Trans Circuits Syst II Express Briefs 69(3):1652–1656. https://doi.org/10.1109/TCSII.2021.3120495
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