Dynamically Reconfigurable Perception using Dataflow Parameterization of Channel Attention-Reference-Cited by-同舟云学术

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Dynamically Reconfigurable Perception using Dataflow Parameterization of Channel Attention

Published:2023-10-29 Issue: Volume: Page:747-754
ISSN:
Container-title:2023 57th Asilomar Conference on Signals, Systems, and Computers
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Author:

Mal Yujunrong¹,Nikha Kshitij²,Boutellier Jani³,Riggan Benjamin⁴,Bhattacharyya Shuvra S.⁵

Affiliation:

1. University of Maryland,Department of Electrical and Computer Engi-neering,USA

2. University of Nebraska-Lincoln,Department of Electrical and Computer Engineering,USA

3. School of Technology and Innovation, University of Vaasa,Finland

4. University of Nebraska-Lincoln,Department of Electrical and Computer Engi-neering,USA

5. Institute for Advanced Computer Studies, University of Maryland,Department of Electrical and Computer Engineering,College Park,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10476702/10476779/10476908.pdf?arnumber=10476908

Reference31 articles.

1. Very deep convolutional networks for large-scale image recognition;Simonyan;arXiv preprint,2014

2. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

3. Deep Residual Learning for Image Recognition

4. Resource-constrained implementation and optimization of a deep neural network for vehicle classification

5. Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators

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