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 ...
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 ...
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 ...
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 ...
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 ...
The objective of this tutorial is to present the fundamental theory of Karp, Miller and Winograd, whose seminal paper laid the foundations regarding the systematic description of the organization of ...
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