Compression-Aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations

Author:

Tai Yu-Shan1,Teng Chieh-Fang1,Chang Cheng-Yang1,Wu An-Yeu Andy1

Affiliation:

1. National Taiwan University,Graduate Institute of Electrical Engineering,Taipei,Taiwan

Funder

Nova

Ministry of Science and Technology

Publisher

IEEE

Cited by 5 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. Joint Optimization of Dimension Reduction and Mixed-Precision Quantization for Activation Compression of Neural Networks;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023-11

2. Surround the Nonlinearity: Inserting Foldable Convolutional Autoencoders to Reduce Activation Footprint;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02

3. TSPTQ-ViT: Two-Scaled Post-Training Quantization for Vision Transformer;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

4. Improved Projection Learning for Lower Dimensional Feature Maps;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

5. TECO: A Unified Feature Map Compression Framework Based on Transform and Entropy;IEEE Transactions on Neural Networks and Learning Systems;2023

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