VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization

18 May 2020

Papers citing "VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization"

6 / 6 papers shown

Title
Hybrid-Parallel: Achieving High Performance and Energy Efficient Distributed Inference on Robots Zekai Sun Xiuxian Guan Junming Wang Haoze Song Yuhao Qing Tianxiang Shen Dong Huang Fangming Liu Heming Cui 34 0 0 29 May 2024
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks Cheng Gong Ye Lu Surong Dai Deng Qian Chenkun Du Tao Li MQ 27 0 0 07 Apr 2023
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks Cheng Gong Ye Lu Kunpeng Xie Zongming Jin Tao Li Yanzhi Wang MQ 25 7 0 08 Sep 2021
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration Yao Chen Cole Hawkins Kaiqi Zhang Zheng-Wei Zhang Cong Hao 18 8 0 11 May 2021
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design Cong Hao Jordan Dotzel Jinjun Xiong Luca Benini Zhiru Zhang Deming Chen 50 34 0 25 Mar 2021
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights Aojun Zhou Anbang Yao Yiwen Guo Lin Xu Yurong Chen MQ 319 1,049 0 10 Feb 2017