IEEE

Evaluating Winograd Algorithm for Convolution Neural Network using Verilog

Abstract: Convolutional neural networks (CNNs) are very resource intensive and consume a lot of computational power. The convolution operation itself is a very complex process. Hence this work deals ...
IEEE

Vectorized Winograd’s algorithm for Convolution Neural networks

Abstract: Winograd’s algorithm has demonstrated its advantages in accelerating the inference of convolution neural networks. It reduces the number of multiplications in convolution and has achieved ...
Europe PMC

Optimizing FPGA-Based Target Detection: A Hybrid Winograd-GEMM Accelerator with Enhanced Resource Efficiency and Throughput

To store the constant transformation matrix while using the classic Winograd algorithm on FPGAs, a large amount of on-chip resources needs to be used, which will reduce the model’s throughput and ...
Scientific Research Publishing

Winograd, S. (1968) A New Algorithm for Inner Product. IEEE Transactions on Computers, 17, 693-694.

ABSTRACT: The first error theory and bounds for Fast Matrix Multiplication based on the Strassen-Winograd algorithms (FastMMW) were formulated in the 70s. The theory ...
GitHub

Efficient Sparse-Winograd Convolutional Neural Networks

This is the code and models for paper Efficient Sparse-Winograd Convolutional Neural Networks by Xingyu Liu et al. This work is based on our ICLR 2018 paper. We propose modifications to Winograd-based ...