Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardwar...Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.展开更多
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.2022ZTE09.
文摘Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.
文摘为了提高小目标识别和分类的实时性,同时降低识别系统的资源消耗,本文提出了一种简易、高效的现场可编程门阵列(Field Programmable Gate Array,FPGA)小目标识别分类系统。该系统首先通过图像预处理消除图像噪点,并采用并行计算提升系统实时性。然后将处理后的图像与模板进行匹配计算得到识别结果,设计的模板匹配电路具有较小的硬件复杂度和较快的处理速度。实验结果表明,本文所提出的识别系统在680×480图像分辨下,可达137.5帧/s的处理速度,实时性强,同时仅消耗了9个块随机存储器(Block Random Access Memory,BRAM)和2个数字信号处理器(Digital Signal Processor,DSP),硬件资源消耗较少,在处理小目标识别和分类问题上有较好的实用价值。