Reduction of Multiplications in Convolutional Neural Networks

Convolution neural network (CNN) widely used for the application of computer vision, such applications would be beneficial for CNN if their workload could be reduced. In this paper, we evaluate CNN's arithmetic properties (Matrix Multiplications and Additions) and proposed a Strassen algorithm...

Full description

Saved in:
Bibliographic Details
Published inChinese Control Conference pp. 7406 - 7411
Main Authors Ali, Munawar, Yin, Baoqun, Kunar, Aakash, Sheikh, Ali Muhammad, Bilal, Hazrat
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 01.07.2020
Subjects
Complexity theory
Convolution
Convolutional neural networks
Mathematical model
Matrix Multiplication
Memory management
Runtime
Strassen-Winograd algorithm
Online AccessGet full text

Cover

More Information
Summary:Convolution neural network (CNN) widely used for the application of computer vision, such applications would be beneficial for CNN if their workload could be reduced. In this paper, we evaluate CNN's arithmetic properties (Matrix Multiplications and Additions) and proposed a Strassen algorithm with the help of Pan's result to minimize their workload of computation. That algorithm needs the least workspace and it has great computational accuracy. Matrix multiplication (MM) is the most fundamental computational operation, and the performance of Matrix multiplication (MM) depends on different factors. Moreover, that is a very flexible and robust structure. Here we are focusing on the number of element-wise multiplications and addition, the effects of this research on modern computations.
ISSN:1934-1768
DOI:10.23919/CCC50068.2020.9188843