Scale-out Acceleration for 3D CNN-based Lung Nodule Segmentation on a Multi-FPGA System.

Autor:	Junzhong Shen, Deguang Wang, You Huang, Mei Wen, Chunyuan Zhang
Předmět:	CONVOLUTIONAL neural networks FIELD programmable gate arrays COMPUTATIONAL complexity SYSTEM integration GRAPHICS processing units ARTIFICIAL neural networks
Zdroj:	DAC: Annual ACM/IEEE Design Automation Conference; 2019, Issue 56, p991-996, 6p
Abstrakt:	Three-dimensional convolutional neural networks (3D CNNs) have become a promising method in lung nodule segmentation. The high computational complexity and memory requirements of 3D CNNs make it challenging to accelerate 3D CNNs on a single FPGA. In this work, we focus on accelerating the 3D CNN-based lung nodule segmentation on a multi-FPGA platform by proposing an efficient mapping scheme that takes advantage of the massive parallelism provided by the platform, as well as maximizing the computational efficiency of the accelerators. Experimental results show that our system integrating with four Xilinx VCU118 can achieve state-of-the-art performance of 14.5 TOPS, in addition with a 29.4x performance gain over CPU and 10.5x more energy efficiency over GPU. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu