ViT-LCA: A Neuromorphic Approach for Vision Transformers

• URL: http://arxiv.org/abs/2411.00140v1
• Date: Thu, 31 Oct 2024 18:41:30 GMT
• Title: ViT-LCA: A Neuromorphic Approach for Vision Transformers
• Authors: Sanaz Mahmoodi Takaghaj,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: 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.

Related papers

• Self-Supervised Pre-Training for Table Structure Recognition Transformer [25.04573593082671]
  We propose a self-supervised pre-training (SSP) method for table structure recognition transformers. We discover that the performance gap between the linear projection transformer and the hybrid CNN-transformer can be mitigated by SSP of the visual encoder in the TSR model.
  arXiv  Detail & Related papers  (2024-02-23T19:34:06Z)
• Multi-Dimensional Hyena for Spatial Inductive Bias [69.3021852589771]
  We present a data-efficient vision transformer that does not rely on self-attention. Instead, it employs a novel generalization to multiple axes of the very recent Hyena layer. We show that a hybrid approach that is based on Hyena N-D for the first layers in ViT, followed by layers that incorporate conventional attention, consistently boosts the performance of various vision transformer architectures.
  arXiv  Detail & Related papers  (2023-09-24T10:22:35Z)
• ModeT: Learning Deformable Image Registration via Motion Decomposition Transformer [7.629385629884155]
  We propose a novel motion decomposition Transformer (ModeT) to explicitly model multiple motion modalities. Our method outperforms current state-of-the-art registration networks and Transformers.
  arXiv  Detail & Related papers  (2023-06-09T06:00:05Z)
• A survey of the Vision Transformers and their CNN-Transformer based Variants [0.48163317476588563]
  Vision transformers have become popular as a possible substitute to convolutional neural networks (CNNs) for a variety of computer vision applications. These transformers, with their ability to focus on global relationships in images, offer large learning capacity. Recently, in vision transformers hybridization of both the convolution operation and self-attention mechanism has emerged, to exploit both the local and global image representations.
  arXiv  Detail & Related papers  (2023-05-17T01:27:27Z)
• HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling [126.89573619301953]
  We propose a new design of hierarchical vision transformers named HiViT (short for Hierarchical ViT) HiViT enjoys both high efficiency and good performance in MIM. In running MAE on ImageNet-1K, HiViT-B reports a +0.6% accuracy gain over ViT-B and a 1.9$times$ speed-up over Swin-B.
  arXiv  Detail & Related papers  (2022-05-30T09:34:44Z)
• Can Vision Transformers Perform Convolution? [78.42076260340869]
  We prove that a single ViT layer with image patches as the input can perform any convolution operation constructively. We provide a lower bound on the number of heads for Vision Transformers to express CNNs.
  arXiv  Detail & Related papers  (2021-11-02T03:30:17Z)
• Augmented Shortcuts for Vision Transformers [49.70151144700589]
  We study the relationship between shortcuts and feature diversity in vision transformer models. We present an augmented shortcut scheme, which inserts additional paths with learnable parameters in parallel on the original shortcuts. Experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2021-06-30T09:48:30Z)
• Visformer: The Vision-friendly Transformer [105.52122194322592]
  We propose a new architecture named Visformer, which is abbreviated from the Vision-friendly Transformer' With the same computational complexity, Visformer outperforms both the Transformer-based and convolution-based models in terms of ImageNet classification accuracy.
  arXiv  Detail & Related papers  (2021-04-26T13:13:03Z)
• A Survey on Visual Transformer [126.56860258176324]
  Transformer is a type of deep neural network mainly based on the self-attention mechanism. In this paper, we review these vision transformer models by categorizing them in different tasks and analyzing their advantages and disadvantages.
  arXiv  Detail & Related papers  (2020-12-23T09:37:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.