ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks

5 November 2018

Papers citing "ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks"

8 / 8 papers shown

Title
ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs Yuchen Yang Shubham Ugare Yifan Zhao Gagandeep Singh Sasa Misailovic MQ 21 0 0 31 Oct 2024
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search Jordan Dotzel Gang Wu Andrew Li M. Umar Yun Ni ... Liqun Cheng Martin G. Dixon N. Jouppi Quoc V. Le Sheng R. Li MQ 22 3 0 07 Aug 2023
Mixed-Precision Neural Networks: A Survey M. Rakka M. Fouda Pramod P. Khargonekar Fadi J. Kurdahi MQ 14 11 0 11 Aug 2022
Learnable Mixed-precision and Dimension Reduction Co-design for Low-storage Activation Yu-Shan Tai Cheng-Yang Chang Chieh-Fang Teng AnYeu A. Wu 14 5 0 16 Jul 2022
GeneCAI: Genetic Evolution for Acquiring Compact AI Mojan Javaheripi Mohammad Samragh T. Javidi F. Koushanfar 24 9 0 08 Apr 2020
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation Byung Hoon Ahn Prannoy Pilligundla Amir Yazdanbakhsh H. Esmaeilzadeh ODL 53 80 0 23 Jan 2020
Towards Efficient Training for Neural Network Quantization Qing Jin Linjie Yang Zhenyu A. Liao MQ 11 42 0 21 Dec 2019
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 264 5,290 0 05 Nov 2016