CSC3050 RISC-V Simulator with RVV

CSC3050 Project 3: RISC-V Simulator with RVV

1 Background

RISC-V, an open standard instruction set architecture (ISA), has rapidly become a

pivotal force in academic research and industrial development due to its flexibility

and open-source nature. Unlike proprietary ISAs, RISC-V offers the freedom for

developers to customize and extend the architecture, making it an ideal platform

for innovation in research, education, and the design of specialized hardware. One

of its most impactful extensions is the RISC-V Vector Extension (RVV), which

introduces efficient vector processing capabilities—a cornerstone of modern high performance computing. This is especially critical for applications like machine

learning, cryptography, and scientific simulations, where parallel data processing is

essential for improving computational speed and efficiency.

In this project, you are tasked with extending the QTRVSim RISC-V simulator

to support vector operations by implementing some of the RVV instructions.

After reviewing the number of cycles, you will get a feeling of how this is faster

than conducting element-wise operations.

Start early, this project can be time-consuming if you are not familiar with

simulators.

2 QTRVSim

QTRVSim is a RISC-V CPU simulator for education, where you can try its online

version on this link. Just in case you want to try different instructions, you can refer

to this page: RISC-V Instruction Set Specifications. A helpful video about using

QTRVSim can be found 代写CSC3050  RISC-V Simulator with RVV on Youtube

After familiarizing yourself with the QtRVSim manual, you can begin planning how

to integrate RVV instructions into the existing implementation. The simulator’s

source code, written in C++ and including both the core simulation functions and

graphical user interfaces (GUIs), can be found in the repository at this link. To test

your modifications, QtRVSim offers two

【电能质量扰动】基于ML和DWT的电能质量扰动分类方法研究(Matlab实现)内容概要:本文研究了一种基于机器学习(ML)和离散小波变换(DWT)的电能质量扰动分类方法,并提供了Matlab实现方案。首先利用DWT对电能质量信号进行多尺度分解,提取信号的时频域特征,有效捕捉电压暂降、暂升、中断、谐波、闪变等常见扰动的关键信息;随后结合机器学习分类器(如SVM、BP神经网络等)对提取的特征进行训练与分类,实现对不同类型扰动的自动识别与准确区分。该方法充分发挥DWT在信号去噪与特征提取方面的优势,结合ML强大的模式识别能力,提升了分类精度与鲁棒性,具有较强的实用价值。; 适合人群:电气工程、自动化、电力系统及其自动化等相关专业的研究生、科研人员及从事电能质量监测与分析的工程技术人员;具备一定的信号处理基础和Matlab编程能力者更佳。; 使用场景及目标:①应用于智能电网中的电能质量在线监测系统,实现扰动类型的自动识别;②作为高校或科研机构在信号处理、模式识别、电力系统分析等课程的教学案例或科研实验平台;③目标是提高电能质量扰动分类的准确性与效率,为后续的电能治理与设备保护提供决策依据。; 阅读建议:建议读者结合Matlab代码深入理解DWT的实现过程与特征提取步骤,重点关注小波基选择、分解层数设定及特征向量构造对分类性能的影响,并尝试对比不同机器学习模型的分类效果,以全面掌握该方法的核心技术要点。
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