# Energy-efficient Spiking Neural Network Equalization for IM/DD Systems with Optimized Neural Encoding

arXiv: https://arxiv.org/abs/2312.12909 · HTML: https://mlsanity.com/paper/2312.12909/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [SPOQ ℓp-Over-ℓq Regularization for Sparse Signal Recovery applied to Mass Spectrometry](https://mlsanity.com/paper/2001.08496/) (2020)
- [Spiking Neural Network Equalization for IM/DD Optical Communication](https://mlsanity.com/paper/2205.04263/) (2022)
- [Spiking Neural Network Decision Feedback Equalization](https://mlsanity.com/paper/2211.04756/) (2022)
- [Spiking Neural Network Decision Feedback Equalization for IM/DD Systems](https://mlsanity.com/paper/2304.14152/) (2023)

## Built on by (most cited first, 1 of 1)

- [Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications](https://mlsanity.com/paper/2411.10101/) (2024)

## Abstract (arXiv)

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.