article · Zenodo (CERN European Organization for Nuclear Research) · 2025

Implementation Of Approximate SOFTMAX Function For Neural Network

Yuvarani S, Yuvaraj V, Sakthi Siddharth S, Vikram S, Saran M

Cited by 0Open access

Abstract

The IEEE 754 floating-point standard is fundamental in digital computing, enabling efficient numerical operations across various applications. This study presents a novel VLSI-based hardware architecture for the Softmax activation function, an essential component in deep neural networks (DNNs). The Softmax function converts raw neural network outputs into probability distributions for classification tasks. However, traditional hardware implementations suffer from high power consumption, latency, and area inefficiency due to the complexity of floating-point division and exponential computations. To address these challenges, we propose a highperformance VLSI design incorporating a pipelined clockbased division technique and an optimized exponential computation unit. The architecture consists of three key modules: (i) an Exponent Module that efficiently computes exponentials using IEEE 754 floating-point representation, (ii) an Adder Tree that rapidly sums exponentials with minimized delay and area, and (iii) a Pipelined Divider that performs normalization efficiently. The pipelined division approach significantly reduces execution time while maintaining high precision. Additionally, we explore an approximate Softmax implementation to achieve further improvements in speed and resource utilization. Comparative analysis with traditional Softmax implementations highlights improvements in power efficiency, accuracy, and latency. Experimental results demonstrate that our proposed design effectively balances computational complexity with hardware efficiency, making it suitable for real-time deep learning applications. This work contributes to enhancing neural network hardware accelerators by reducing energy consumption and improving execution speed, ensuring optimal performance in edge and embedded AI systems.

Cite this paper

APA 7

S, Y., V, Y., S, S. S., S, V., & M, S. (2025). Implementation Of Approximate SOFTMAX Function For Neural Network. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.17562927

MLA 9

S, Yuvarani, et al. “Implementation Of Approximate SOFTMAX Function For Neural Network.” Zenodo (CERN European Organization for Nuclear Research), 2025, https://doi.org/10.5281/zenodo.17562927.

Chicago

S, Yuvarani, Yuvaraj V, Sakthi Siddharth S, Vikram S, and Saran M. 2025. “Implementation Of Approximate SOFTMAX Function For Neural Network.” Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.17562927.

Harvard

S, Y. et al. (2025) ‘Implementation Of Approximate SOFTMAX Function For Neural Network’, Zenodo (CERN European Organization for Nuclear Research). Available at: https://doi.org/10.5281/zenodo.17562927.

BibTeX

@article{s2025implementation,
  title = {{Implementation Of Approximate SOFTMAX Function For Neural Network}},
  author = {S, Yuvarani and V, Yuvaraj and S, Sakthi Siddharth and S, Vikram and M, Saran},
  journal = {Zenodo (CERN European Organization for Nuclear Research)},
  year = {2025},
  doi = {10.5281/zenodo.17562927}
}

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