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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Hardware Engineering

    Background:

    • The sigmoid function is crucial for neural networks, especially on edge devices.
    • Previous probabilistic approximations using Gaussian cumulative functions faced challenges with memory usage, speed, and accuracy across all inputs.

    Purpose of the Study:

    • To develop a hardware-friendly and highly accurate probabilistic sigmoid approximator.
    • To overcome the limitations of existing methods, such as high RAM requirements and time-consuming processes.

    Main Methods:

    • Establishing the equivalence between sigmoid function output and the probability of a logistic random variable.
    • Implementing an indirect random variable quantizing strategy to minimize memory and precision loss.
    • Optimizing latency and developing a resource-efficient digital circuit implementation.

    Main Results:

    • The proposed scheme significantly reduces memory usage and precision loss.
    • The developed sigmoid approximator demonstrates optimized latency and resource efficiency.
    • An upper bound on the absolute error was derived, confirming approximation accuracy.

    Conclusions:

    • The novel probabilistic sigmoid approximator offers superior performance in accuracy and resource cost compared to existing methods.
    • This approach provides a hardware-friendly and efficient solution for sigmoid approximation in neural networks, particularly for edge computing applications.