Published June 13, 2018 | Version v1
Dataset Open

Robustness assessment through iterative statistical fault injection: LEON3 processor as a case study

  • 1. Universitat Politècnica de València

Description

Dataset exemplifies an approach of iterative statistical fault injection to assess the robustness of HDL models.

Contents:
1. Results of exhaustive fault injection experiments (bit-flip faults) into LEON3 processor model;
2. Interactive querying interface, allowing to obtain custom samples from exhaustive results, and visualize them;
3. Python scripts simulating 3 approaches to statistical fault injection: conservative, error-driven, time-driven.

 

Installation guide:

        1. Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. 
        Otherwise download and install 2.x.x distribution: https://www.python.org/
        Add python installation path to environment path variable.


        2. Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html

          
        3. Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:
        In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):
         

        – search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: 
        Options Indexes FollowSymLinks ExecCGI
        – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the results looks like:
        AddHandler cgi-script .cgi .pl .asp .py 

        4. Unpack the contents of *.zip package into the folder on the Web Server. 
        For instance into 'Web-server root folder'/Dataset.
        The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: 
        DocumentRoot "F:/HTWEB"
        <Directory "F:/HTWEB">
        ...

        5. In the web-browser navigate to the root directory of extracted package:
        http://localhost/Dataset/index.html

Files

DATASET.zip

Files (45.6 MB)

Name Size Download all
md5:bb4dba7122c7db1c124308ba8935ac25
45.6 MB Preview Download