Efficient Execution of Quantized Deep Learning Models: A Compiler Approach

18 June 2020

Papers citing "Efficient Execution of Quantized Deep Learning Models: A Compiler Approach"

4 / 4 papers shown

Title
Decompiling x86 Deep Neural Network Executables Zhibo Liu Yuanyuan Yuan Shuai Wang Xiaofei Xie Lei Ma AAML 45 13 0 03 Oct 2022
Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment Jemin Lee Misun Yu Yongin Kwon Teaho Kim MQ 27 17 0 10 Feb 2022
Automated Backend-Aware Post-Training Quantization Ziheng Jiang Animesh Jain An Liu Josh Fromm Chengqian Ma Tianqi Chen Luis Ceze MQ 37 2 0 27 Mar 2021
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case F. Rehm S. Vallecorsa V. Saletore Hans Pabst Adel Chaibi V. Codreanu Kerstin Borras D. Krücker MQ 19 16 0 18 Mar 2021