# CHAMP: Coherent Hardware-Aware Magnitude Pruning of Integrated Photonic Neural Networks

arXiv: https://arxiv.org/abs/2112.06098 · HTML: https://mlsanity.com/paper/2112.06098/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 3 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Built on by (most cited first, 2 of 2)

- [Characterizing Coherent Integrated Photonic Neural Networks under Imperfections](https://mlsanity.com/paper/2207.10835/) (2022)
- [Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics](https://mlsanity.com/paper/2401.06885/) (2024)

## Abstract (arXiv)

We propose a novel hardware-aware magnitude pruning technique for coherent photonic neural networks. The proposed technique can prune 99.45% of network parameters and reduce the static power consumption by 98.23% with a negligible accuracy loss.