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FPGA Implementation for the Sigmoid with Piecewise Linear Fitting Method Based on Curvature Analysis
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Accuracy Evaluation of Long Short Term Memory Network Based Language Model with Fixed-Point Arithmetic
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Machine Learning Based Resource Utilization and Pre-estimation for Network on Chip (NoC) Communication
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3D Multilayer Mesh NoC Communication and FPGA Synthesis
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Low-error digital hardware implementation of artificial neuron activation functions and their derivative
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Controlled accuracy approximation of sigmoid function for efficient FPGA‐based implementation of artificial neurons
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Accuracy controlled iterative method for efficient sigmoid function approximation
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Precise digital implementations of hyperbolic tanh and sigmoid function
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Accelerating Database Systems Using FPGAs: A Survey
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AI Accelerator Survey and Trends
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Challenges in implementation of ANN in embedded system
inferredcites →https://doi.org/10.1109/iscas.2009.5118213openalex-snapshot
Efficient hardware implementation of the hyperbolic tangent sigmoid function
inferredcites →https://doi.org/10.1109/ises52644.2021.00036openalex-snapshot
VLSI Architecture of Sigmoid Activation Function for Rapid Prototyping of Machine Learning Applications
inferredcites →https://doi.org/10.1109/tcad.2019.2897701openalex-snapshot
Evaluating Fast Algorithms for Convolutional Neural Networks on FPGAs
inferredcites →https://doi.org/10.1109/tie.2022.3146573openalex-snapshot
A Modular Approximation Methodology for Efficient Fixed-Point Hardware Implementation of the Sigmoid Function
inferredcites →https://doi.org/10.1109/tmscs.2017.2754378openalex-snapshot
Scalable Light-Weight Integration of FPGA Based Accelerators with Chip Multi-Processors
inferredcites →https://doi.org/10.1109/tnn.2006.883002openalex-snapshot
The Impact of Arithmetic Representation on Implementing MLP-BP on FPGAs: A Study
inferredcites →https://doi.org/10.1109/tvlsi.2016.2615306openalex-snapshot
An Analog CVNS-Based Sigmoid Neuron for Precise Neurochips
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Machine Learning for Survival Analysis
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A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration
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An Efficient Hardware Architecture with Adjustable Precision and Extensible Range to Implement Sigmoid and Tanh Functions
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Hardware Acceleration of Activation Functions Using Piecewise-Legendre Polynomial Approximation Technique
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Calculation of the Sigmoid Activation Function in FPGA Using Rational Fractions
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Leveraging Application-Specific Knowledge for Energy-Efficient Deep Learning Accelerators on Resource-Constrained FPGAs
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Efficient Integer-Only Implementation of Tanh and Sigmoid for Embedded AI on RISC-V
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FAST: FPGA Acceleration of Neural Networks Training
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Approximation Approaches for Efficient Hardware Implementation of Sigmoid and Hyperbolic Tangent Activation Function on FPGA: A Comparison Study
inferred← citeshttps://doi.org/10.1109/iciis69028.2026.11450914openalex-snapshot
Hardware Acceleration of Activation Functions using Piecewise-Legendre Polynomial Approximation Technique
inferred← citeshttps://doi.org/10.1109/iscas56072.2025.11043961openalex-snapshot
Approximating Nonlinear Activation Function Using Genetic Programming
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Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays
inferred← citeshttps://doi.org/10.1109/raics66191.2025.11332583openalex-snapshot
Optimization of Sigmoid Activation Function on FPGA: An Analysis of Linear Interpolation with Fixed-Point Representations
inferred← citeshttps://doi.org/10.1109/tc.2026.3700077openalex-snapshot
FlexPWL: A Flexible, Scalable, and Multiplier-Free Approach for Activation Functions on FPGA
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A Fully Probabilistic Model for Sigmoid Approximation and Its Hardware- Efficient Implementation
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Precision-Specific Efficient Designs and FPGA Implementation of Sigmoid for Machine Learning
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An Area-Efficient and Low-Error FPGA-Based Sigmoid Function Approximation
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