ReAFM: A Reconfigurable Nonlinear Activation Function Module for Neural Networks
Author:
Affiliation:
1. School of Electronic Science and Engineering, Nanjing University, Nanjing, China
Funder
National Natural Science Foundation of China
High-Level Personnel Project of Jiangsu Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/8920/10164655/10034673.pdf?arnumber=10034673
Reference16 articles.
1. A High-Precision Flexible Symmetry-Aware Architecture for Element-Wise Activation Functions
2. A Universal Method of Linear Approximation With Controllable Error for the Efficient Implementation of Transcendental Functions
3. LETA: A lightweight exchangeable-track accelerator for efficientnet based on FPGA
4. An Efficient Hardware Architecture for Activation Function in Deep Learning Processor
5. A Novel Approximation Methodology and Its Efficient VLSI Implementation for the Sigmoid Function
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1. A reconfigurable multi-precision quantization-aware nonlinear activation function hardware module for DNNs;Microelectronics Journal;2024-09
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