Hardware Realization of Nonlinear Activation Functions for NN-based Optical Equalizers
arXiv:2305.09495v1
Originality Synthesis-oriented
AI Analysis
This work addresses hardware efficiency for optical communication systems, but it is incremental as it focuses on approximation rather than a fundamental breakthrough.
The paper tackled the challenge of reducing hardware complexity in neural network-based optical channel equalizers by approximating activation functions, achieving performance close to the original model.
To reduce the complexity of the hardware implementation of neural network-based optical channel equalizers, we demonstrate that the performance of the biLSTM equalizer with approximated activation functions is close to that of the original model.