Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› dac-reproducibility
dac-reproducibility
GitHub用于构建DAC论文的可复现性故事，涵盖固定EDA基准测试、工具版本披露、PDK信息、随机流种子报告及匿名化代码仓库路径，确保结果可信且可复现。
Trigger Scenarios
撰写DAC/IEEE设计自动化会议研究手稿
需要建立EDA实验的可复现性证明
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill dac-reproducibility -g -y
SKILL.md
Frontmatter
{
"name": "dac-reproducibility",
"description": "Use when building the reproducibility story for an ACM\/IEEE Design Automation Conference (DAC) Research Manuscript, covering pinned EDA benchmark suites and versions (ISPD, EPFL, ISCAS\/ITC, TAU, CircuitNet), open-source flow provenance (OpenROAD, ABC, Yosys), PDK\/library and tool-version disclosure, seed\/variance reporting for stochastic and ML flows, and the anonymized-then-public repository path — absent a formal DAC artifact-badging track."
}
DAC Reproducibility
Build the reproducibility story into the evaluation, not onto it. DAC does not run a formal, badge-issuing artifact-evaluation track for research manuscripts (待核实 per cycle), so reproducibility at DAC is not a review checkbox — it is what makes your QoR numbers credible to a skeptical EDA reviewer and usable by the community that cites you. The currency is pinned benchmarks, disclosed tool versions, and re-runnable flows.
The EDA reproducibility floor
- Pin the benchmark suite and version. Name the exact suite (ISPD 2005/2015 contests, the EPFL combinational suite, ISCAS'85/'89, ITC'99, a TAU contest set, CircuitNet/OpenABC-D) and the specific release. "Standard benchmarks" without a version is not reproducible.
- Disclose the flow and tool versions. State the EDA tools and versions used — open (OpenROAD, ABC, Yosys, KLayout) or commercial — because QoR depends heavily on the flow. If a commercial tool or PDK is under NDA, say which class of tool it is and give what you can.
- Name the PDK / technology / library. QoR numbers are meaningless without the technology node and standard-cell library context (e.g., an open Nangate/ASAP7 PDK, or a named foundry node under NDA). Report the node and library or the reason you cannot.
- Report the hardware and runtime. The machine, core count, and memory for every runtime number; a runtime with no hardware context cannot be compared.
- Pin data provenance for ML-for-EDA. Dataset name and version, the train/test design split, and cached generated data — a model that needs re-generated data or per-design retraining must say so.
Seeds, variance, and stochastic flows
Many EDA flows are stochastic (simulated annealing, partitioning, RL-based placement/routing). A single run is not reproducible evidence:
[Seeds] report the seeds used and fix them where the tool allows
[Runs] multiple runs; report mean and variance/spread, not a lucky best
[Determinism] note where the flow is nondeterministic (threading, tie-breaking) and how you handled it
[Environment] container or pinned dependency list so a re-runner gets the same tool behavior
The anonymized-then-public repository path
- At submission (double-blind): if you link code/data, anonymize it exactly like the PDF — no
author/lab names, no personal GitHub, no cluster paths, no vendor fingerprints (
../dac-submission). Reviewers may not open it, so it strengthens but cannot rescue the paper. - After acceptance: publish the de-anonymized repository, ideally with a DOI-issuing archive (Zenodo/Software Heritage) for a stable citation, an OSI license, and a README that maps each paper claim to the script and benchmark that produce it. This is community goodwill and citation insurance, not a DAC badge.
Claim-to-reproduction mapping
Even without a review requirement, build the mapping that makes your numbers checkable:
| Paper claim | What reproduces it |
|---|---|
| "X% wirelength on ISPD" | The exact ISPD release + your tool version + the run script + seeds |
| "Y% timing improvement" | The design set + PDK/library + STA tool version + the flow script |
| "ML predicts IR-drop with error E" | The dataset version + train/test split + the model checkpoint |
| "Runs in Z hours at N cells" | The hardware spec + the largest-benchmark log |
What DAC-specific reproducibility is not
- It is not an ACM artifact-badging exercise (DAC has no standing badge track); do not design for a badge that does not exist.
- It is not empirical-SE data availability; DAC evidence is QoR on circuits, so provenance means benchmark/PDK/tool versions and seeds, not human-subject protocols.
- It is not optional for credibility: an EDA reviewer distrusts a QoR claim that no one else could reproduce, even where no rule compels an artifact.
Output format
[Reproducibility readiness] strong / adequate / weak
[Benchmarks pinned] suite + version named? yes/no
[Flow disclosed] tool versions + PDK/library + hardware reported? yes/no
[Stochasticity] seeds + variance across runs reported? yes/no
[ML provenance] dataset version + train/test split + cached data? yes/no/NA
[Repo path] anonymized at review / DOI-archived + licensed after accept? planned? yes/no
Version History
- 9f86f09 Current 2026-07-19 15:12
