dac-reproducibility

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