Efficient Winograd or Cook-Toom Convolution Kernel Implementation on
Widely Used Mobile CPUs

Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs

4 March 2019

Ganesh S. Dasika

Matthew Mattina

Robert D. Mullins

Papers citing "Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs"

12 / 12 papers shown

Title
YFlows: Systematic Dataflow Exploration and Code Generation for Efficient Neural Network Inference using SIMD Architectures on CPUs Cyrus Zhou Zack Hassman Ruize Xu Dhirpal Shah Vaughn Richard Yanjing Li 32 1 0 01 Oct 2023
On Efficient Training of Large-Scale Deep Learning Models: A Literature Review Li Shen Yan Sun Zhiyuan Yu Liang Ding Xinmei Tian Dacheng Tao VLM 28 40 0 07 Apr 2023
Accelerating CNN inference on long vector architectures via co-design Sonia Rani Gupta Nikela Papadopoulou Miquel Pericàs 3DV 11 4 0 22 Dec 2022
Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile Renzo Andri Beatrice Bussolino A. Cipolletta Lukas Cavigelli Zhe Wang MQ 26 13 0 26 Sep 2022
Winograd Convolution for Deep Neural Networks: Efficient Point Selection Syed Asad Alam Andrew Anderson B. Barabasz David Gregg 56 25 0 25 Jan 2022
Fast Convolution based on Winograd Minimum Filtering: Introduction and Development Gan Tong Libo Huang 13 2 0 01 Nov 2021
Automated Design Space Exploration for optimised Deployment of DNN on Arm Cortex-A CPUs Miguel de Prado Andrew Mundy Rabia Saeed Maurizo Denna Nuria Pazos Luca Benini 16 11 0 09 Jun 2020
Quantaized Winograd/Toom-Cook Convolution for DNNs: Beyond Canonical Polynomials Base B. Barabasz MQ 10 5 0 23 Apr 2020
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units Guangli Li Lei Liu Xueying Wang Xiu Ma Xiaobing Feng MQ 11 18 0 19 Mar 2020
Searching for Winograd-aware Quantized Networks Javier Fernandez-Marques P. Whatmough Andrew Mundy Matthew Mattina MQ 9 40 0 25 Feb 2020
DWM: A Decomposable Winograd Method for Convolution Acceleration Di Huang Xishan Zhang Rui Zhang Tian Zhi Deyuan He ... Qi Guo Zidong Du Shaoli Liu Tianshi Chen Yunji Chen 8 26 0 03 Feb 2020
A model-driven approach for a new generation of adaptive libraries Marco Cianfriglia Damiano Perri C. Nugteren Anton Lokhmotov G. Fursin 14 14 0 19 Jun 2018