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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19447035 |
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Table of Contents:
- <p> Overview This is the initial release for the <strong>Artifact Evaluation (AE)</strong> of our paper. This repository provides a complete environment and automated scripts to reproduce the performance comparison experiments between different verification methodologies.</p> <p> Key Features</p> <ul> <li>Experiment Group A: Comprehensive comparison among <code>cocotb</code>, <code>python-dpi</code>, <code>python-vpi</code>, and <code>python-mem_direct</code>.</li> <li>Experiment Group B: Multilang performance analysis across <code>raw-verilator</code>, <code>python</code>, <code>cpp</code>, <code>java</code>, and <code>golang</code>.</li> <li>Hardware Targets: Supported DUTs include <code>Rocket (SimTop)</code>, <code>coupledL2 (TestTop)</code>, and <code>XS (SimTop)</code>.</li> <li>Optimized for AE:<ul> <li><strong>Dockerized Environment</strong>: Fully containerized setup via ghcr.io/makiras/unitychipexp:latest or local Dockerfile for a "one-click" experience.</li> </ul> </li> </ul> <p> Quick Start for Reviewers</p> <ol> <li>Pull Docker Image: sudo docker pull ghcr.io/makiras/unitychipexp:latest</li> <li>Run Experiments:<ul> <li>Follow the Reproduction Guide in README.md.</li> <li>Use ./scripts/run_experiments.py to automate the benchmarking process.</li> </ul> </li> <li>Analyze Results: Metrics and logs are automatically generated in the results/ directory.</li> </ol>