# ViT-LCA: A Neuromorphic Approach for Vision Transformers

arXiv: https://arxiv.org/abs/2411.00140 · HTML: https://mlsanity.com/paper/2411.00140/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2024.

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

- [Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net](https://mlsanity.com/paper/2411.14585/) (2024)

## Abstract (arXiv)

The recent success of Vision Transformers has generated significant interest in attention mechanisms and transformer architectures. Although existing methods have proposed spiking self-attention mechanisms compatible with spiking neural networks, they often face challenges in effective deployment on current neuromorphic platforms. This paper introduces a novel model that combines vision transformers with the Locally Competitive Algorithm (LCA) to facilitate efficient neuromorphic deployment. Our experiments show that ViT-LCA achieves higher accuracy on ImageNet-1K dataset while consuming significantly less energy than other spiking vision transformer counterparts. Furthermore, ViT-LCA's neuromorphic-friendly design allows for more direct mapping onto current neuromorphic architectures.