A CNN Hardware Accelerator Using Triangle-based Convolution

Autor:	Amal Thomas K, Soumyajit Poddar, Hemanta Kumar Mondal
Rok vydání:	2022
Předmět:	Hardware and Architecture Electrical and Electronic Engineering Software
Zdroj:	ACM Journal on Emerging Technologies in Computing Systems. 18:1-23
ISSN:	1550-4840 1550-4832
Popis:	Convolutional neural networks (CNNs) have gained a massive impression in the fields of computer vision and especially in the embedded applications because of their high accuracy and performance. However, high computational complexity and power consumption due to convolution operations causes a high demand for low-power accelerators. A 3D geometric optimization strategy is proposed to alleviate the area and power requirements of Multiply Accumulate operations prevalent in all spatial CNNs. The proposed technique is generic and may be easily scaled for accelerators performing spatial 2D convolution.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::533cfe793ab99597bccb739a7e95f442 https://doi.org/10.1145/3544975 Zobrazit plný text záznamu