Single-particle 3D reconstruction on specialized stream architecture and comparison with GPGPUs

The wide acceptance and data deluge in medical imaging processing require faster and more efficient systems to be built.Due to the advances in heterogeneous architectures recently,there has been a resurgence in the first research aimed at FPGA-based as well as GPGPU-based accelerator design.This paper quantitatively analyzes the workload,computational intensity and memory performance of a single-particle 3D reconstruction application,called EMAN,and parallelizes it on CU-DA GPGPU architectures and decouples the memory operations from the computing flow and orches-trates the thread-data mapping to reduce the overhead of off-chip memory operations.Then it exploits the trend towards FPGA-based accelerator design,which is achieved by offloading computingintensive kernels to dedicated hardware modules.Furthermore,a customized memory subsystem is also designed to facilitate the decoupling and optimization of computing dominated data access patterns.This paper evaluates the proposed accelerator design strategies by comparing it with a parallelized program on a 4-cores CPU.The CUDA version on a GTX480 shows a speedup of about 6 times.The performance of the stream architecture implemented on a Xilinx Virtex LX330 FPGA is justified by the reported speedup of 2.54 times.Meanwhile,measured in terms of power efficiency,the FPGA-based accelerator outperforms a 4-cores CPU and a GTX480 by 7.3 times and 3.4 times,respectively.     

基于数学形态学的红外点目标实时检测算法及其CPLD实现

基于数学形态学提出了一种红外图像序列中点目标的检测算法及其硬件系统的实现 .首先介绍了基于数学形态学的点目标检测算法的基本原理 ,然后根据算法的特点提出了它在CPLD上的并行流水线实现方法 ,最后给出并分析了算法软件和硬件系统的仿真结果 .结果表明该方法可以快速、可靠地检测出低信杂比红外图像序列中的点目标 ,且系统结构简单 ,无需存储器 ,适合实际工程中使用