Hardware-Friendly Logarithmic Quantization with Mixed-Precision for MobileNetV2

Author:

Choi Dahun1,Kim Hyun1

Affiliation:

1. Research Center for Electrical and Information Technology Seoul National University of Science and Technology,Department of Electrical and Information Engineering,Seoul,Korea

Funder

National Research Foundation of Korea(NRF)

Publisher

IEEE

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SRU-Q: Hardware-friendly Stochastic Rounding Unit-based Gradient Quantization for CNN Training;2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS);2024-04-22

2. PQDE: Comprehensive Progressive Quantization with Discretization Error for Ultra-Low Bitrate MobileNet towards Low-Resolution Imagery;2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS);2024-04-22

3. ACC: Adaptive Compression Framework for Efficient On-device CNN Training;2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS);2024-04-22

4. DPQ: dynamic pseudo-mean mixed-precision quantization for pruned neural network;Machine Learning;2024-01-31

5. Mixed Precision Quantization with Hardware-Friendly Activation Functions for Hybrid ViT Models;2024 International Conference on Electronics, Information, and Communication (ICEIC);2024-01-28

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