Open AccessResearch on the Architecture of Convolutional Neural Network Accelerator
Author(s) -
Ying Li,
Haitao Ding,
Junhua Ma
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012067
Subject(s) - convolutional neural network , convolution (computer science) , computer science , architecture , mode (computer interface) , artificial neural network , deep learning , artificial intelligence , algorithm , computer engineering , art , visual arts , operating system
Convolutional neural networks are widely used in human production and life, but due to their large amount of calculation and complex calculation mode, their calculation speed is slow, so it is necessary to design a dedicated hardware accelerator. This paper firstly analyzes the algorithm of the convolutional neural network and decomposes the algorithm into multiple basic operations. For the convolution operation with the largest amount of calculation and complex operation mode, a near calculation storage array is designed according to its operational characteristics. Furthermore, a convolutional neural network accelerator architecture is proposed to realize the fast operation of a convolutional neural network.