One Weight Bitwidth to Rule Them All

22 August 2020

Papers citing "One Weight Bitwidth to Rule Them All"

8 / 8 papers shown

Title
Self-Distilled Quantization: Achieving High Compression Rates in Transformer-Based Language Models James OÑeill Sourav Dutta VLM MQ 32 1 0 12 Jul 2023
CSMPQ:Class Separability Based Mixed-Precision Quantization Ming-Yu Wang Taisong Jin Miaohui Zhang Zhengtao Yu MQ 23 0 0 20 Dec 2022
PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers Zhikai Li Mengjuan Chen Junrui Xiao Qingyi Gu ViT MQ 43 33 0 13 Sep 2022
Mixed-Precision Neural Networks: A Survey M. Rakka M. Fouda Pramod P. Khargonekar Fadi J. Kurdahi MQ 18 11 0 11 Aug 2022
A White Paper on Neural Network Quantization Markus Nagel Marios Fournarakis Rana Ali Amjad Yelysei Bondarenko M. V. Baalen Tijmen Blankevoort MQ 19 503 0 15 Jun 2021
Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence Wiebke Toussaint Aaron Yi Ding 15 11 0 01 Dec 2020
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks Zhen Dong Z. Yao Yaohui Cai Daiyaan Arfeen A. Gholami Michael W. Mahoney Kurt Keutzer MQ 26 274 0 10 Nov 2019
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights Aojun Zhou Anbang Yao Yiwen Guo Lin Xu Yurong Chen MQ 313 1,047 0 10 Feb 2017