Energy-efficient Spiking Neural Network Equalization for IM/DD Systems with Optimized Neural Encoding
Alexander von Bank, Eike-Manuel Edelmann, Laurent Schmalen
TL;DR
This paper introduces an energy-efficient spiking neural network equalizer for IM/DD systems, utilizing optimized neural encoding to enhance performance while reducing energy use.
Contribution
It presents a novel neural encoding method that improves equalizer efficiency and energy consumption in IM/DD optical communication systems.
Findings
Enhanced equalization performance with lower energy consumption
Optimized neural encoding significantly boosts neural network efficiency
Potential for practical deployment in energy-constrained optical systems
Abstract
We propose an energy-efficient equalizer for IM/DD systems based on spiking neural networks. We optimize a neural spike encoding that boosts the equalizer's performance while decreasing energy consumption.
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Ferroelectric and Negative Capacitance Devices