Energy-efficient Spiking Neural Network Equalization for IM/DD Systems with Optimized Neural Encoding
arXiv:2312.12909v15 citationsh-index: 5OFC
Originality Incremental advance
AI Analysis
This work addresses energy efficiency in optical communication systems, but appears incremental as it builds on existing spiking neural network methods.
The paper tackled the problem of equalization in IM/DD systems by proposing an energy-efficient spiking neural network equalizer, resulting in improved performance and reduced energy consumption.
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.