Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge-Reference-Cited by-同舟云学术

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Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge

Published:2024-05-19 Issue: Volume: Page:1-5
ISSN:
Container-title:2024 IEEE International Symposium on Circuits and Systems (ISCAS)
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Author:

Dumoulin Joren¹,Houshmand Pouya¹,Jain Vikram¹,Verhelst Marian¹

Affiliation:

1. KU Leuven,MICAS, ESAT

Funder

European Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10557746/10557828/10558587.pdf?arnumber=10558587

Reference25 articles.

1. A ConvNet for the 2020s

2. An image is worth 16x16 words: Transformers for image recognition at scale;Dosovitskiy,2020

3. Deep Residual Learning for Image Recognition

4. Very deep convolutional networks for large-scale image recognition;Simonyan,2014

5. Mobilenets: Efficient convolutional neural networks for mobile vision applications;Howard,2017

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