Session 7b Optimizing Winograd Based Convolution With Tensor Cores

By Editorial Team · October 1, 2026 · 9 min read

Session 7b Optimizing Winograd Based Convolution With Tensor Cores Information Guide

  1. Introduction of Session 7b Optimizing Winograd Based Convolution With Tensor Cores
  2. Key Details
  3. Developments
  4. Expert Insights
  5. Summary

Introduction of Session 7b Optimizing Winograd Based Convolution With Tensor Cores

Details Session 7B: Optimizing Winograd-Based Convolution with Tensor Cores Guide
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Key Details

Full Session 7B: LoWino: Towards Efficient Low Precision Winograd Convolutions on Modern CPUs News
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Developments

Information Session 6B: Optimizing Massively Parallel Winograd Convolution on ARM Processor Update
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Lecture 23: Tensor Cores
Lecture 23: Tensor Cores
tinyML Summit 2021 tiny Talks: Low-precision Winograd Convolution over Residue Number System
tinyML Summit 2021 tiny Talks: Low-precision Winograd Convolution over Residue Number System
DREW: Efficient Winograd CNN Inference with Deep Reuse
DREW: Efficient Winograd CNN Inference with Deep Reuse
CVPR 2022: Channel Balancing for Accurate Quantization of Winograd Convolutions
CVPR 2022: Channel Balancing for Accurate Quantization of Winograd Convolutions
Fast Convolution based on Winograd Minimum Filtering: Introduction and Development
Fast Convolution based on Winograd Minimum Filtering: Introduction and Development
DWM: A Decomposable Winograd Method for Convolution Acceleration
DWM: A Decomposable Winograd Method for Convolution Acceleration
MY152 - Winograd Convolution Accelerator on RISC-V SoC using Rocket Chip
MY152 - Winograd Convolution Accelerator on RISC-V SoC using Rocket Chip
Accelerated Double Double Matrix Multiplication with NVIDIA Tensor Cores
Accelerated Double Double Matrix Multiplication with NVIDIA Tensor Cores
Fast Algorithms for Convolutional Neural Networks
Fast Algorithms for Convolutional Neural Networks
David Gregg - Improving the Accuracy and Speed of Winograd Convolution for Deep Neural Networks
David Gregg - Improving the Accuracy and Speed of Winograd Convolution for Deep Neural Networks
3.7 The Quest for Speed | Efficient Convolution Algorithms | Speeding Up CNNs for Deep Learning
3.7 The Quest for Speed | Efficient Convolution Algorithms | Speeding Up CNNs for Deep Learning

Expert Insights

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Last Updated: October 1, 2026

Summary

[Long version] Accelerating Winograd convolutions using symbolic computation and meta-programming Update
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Summary

This is my presentation for my paper published in EuroSyS 2020 conference related to the acceleration of Slides: drive.google.com/file/d/18sthk6IUOKbdtFphpm_jZNXoJenbWR8m/view?usp=drive_link. tinyML Summit 2021 tinyml.org/event/summit-2021 tinyTalks Algorithms and Tools "Low-precision Systems and Infrastructure: Scalable ML for Web Infrastructure Ruofan Wu, Feng Zhang, Jiawei Guan, Zhen Zheng, Xiaoyong Du ... Official presentation of the CVPR 2022 poster paper "Channel Balancing for Accurate Quantization of Neural Acceleration Study Paper: DWM: A Decomposable In this prerecording for a short talk at the SIAM 2026 Conference on Parallel Processing for Scientific Computing, we consider ... This video is about Fast Algorithms for David Gregg Professor in Computer Science, Trinity College Dublin scss.tcd.ie/David.Gregg ...

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