CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification-Reference-Cited by-同舟云学术

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CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification

Published:2023-07-09 Issue: Volume: Page:
ISSN:
Container-title:2023 60th ACM/IEEE Design Automation Conference (DAC)
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Author:

Xiao Lirui¹,Yang Huanrui²,Dong Zhen²,Keutzer Kurt²,Du Li¹,Zhang Shanghang³

Affiliation:

1. Nanjing University

2. University of California,Berkeley

3. Peking University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10247654/10247655/10247982.pdf?arnumber=10247982

Reference27 articles.

1. Growing efficient deep networks by structured continuous sparsification;yuan,2020

2. Winning the lottery with continuous sparsification;savarese;NeurIPS,2020

3. Ternary weight networks;li,2016

4. Xnor-net: Imagenet classification using binary convolutional neural networks;rastegari;ECCV,2016

5. Estimating or propagating gradients through stochastic neurons for conditional computation;bengio,2013

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1. CIM²PQ: An Arraywise and Hardware-Friendly Mixed Precision Quantization Method for Analog Computing-In-Memory;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2024-07

2. Contemporary Advances in Neural Network Quantization: A Survey;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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