Efficient Inference Of Image-Based Neural Network Models In Reconfigurable Systems With Pruning And Quantization-Reference-Cited by-同舟云学术

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Efficient Inference Of Image-Based Neural Network Models In Reconfigurable Systems With Pruning And Quantization

Published:2022-10-16 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Image Processing (ICIP)
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Author:

Flich Jose¹,Medina Laura¹,Catalan Izan¹,Hernandez Carles¹,Bragagnolo Andrea²,Auzanneau Fabrice³,Briand David³

Affiliation:

1. Universitat Politècnica de València,Spain

2. Università degli Studi di Torino,Dipartimento di Informatica

3. Université Paris Saclay,Palaiseau,France,F-91120

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9897158/9897159/09897752.pdf?arnumber=9897752

Reference22 articles.

1. Fast Algorithms for Convolutional Neural Networks

2. The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

3. Onnx: Open neural network exchange;bai,2019

4. Xception: Deep Learning with Depthwise Separable Convolutions

5. Simplify: A Python library for optimizing pruned neural networks

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2. HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence;Applied Sciences;2023-08-07

3. Towards Efficient Neural Network Model Parallelism on Multi-FPGA Platforms;2023 Design, Automation & Test in Europe Conference & Exhibition (DATE);2023-04

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