svgap 0.3.0a5

Production-evidence profiles for AI-generated digital RTL

SV-Gap

Make the gap between “passes the benchmark” and “reviewable by a chip-design team” explicit.

SV-Gap is an open evaluation layer for AI-generated digital RTL. It preserves the functional result, adds declared design intent and structural evidence, and reports which production questions are answered, failed, or still unknown.

Supply RTL and evaluation evidence. Receive a reviewable account of what that evidence establishes, what it contradicts, and what evidence would resolve the remaining uncertainty.

Choose your first step

Goal	Start here	Time
Understand the result without installing anything	Inspect the controlled result or a public model profile	2 minutes
See the gap execute	docker run --rm ghcr.io/shsridhar-beep/svgap:v0.3.0-alpha.5 demo	2 minutes
Evaluate one model or agent	Run the packaged smoke study	15 minutes
Scope a qualification experiment	Request a research call or email the maintainer	30 minutes

Do not send proprietary RTL or confidential constraints through GitHub or email. A public or synthetic artifact is enough for the first experiment.

What the demo proves

candidate  functional  structural  finding
safe       pass        pass        none
unsafe     pass        fail        REF-RDC-001

Both implementations pass the supplied functional test. Declared reset-release intent and configured structural evidence distinguish them. This is an executable existence result: it is not a defect-rate estimate, certification, or silicon signoff.

Supported today

Surface	Current support	Boundary
Domain	AI-generated digital RTL	Analog and mixed-signal design are out of scope
Initial properties	Documented CDC/RDC structural patterns	Not comprehensive CDC/RDC signoff
Research tracks	Generation, diagnosis, and repair	Profiles remain multidimensional; no scalar leaderboard
Functional evidence	Executed commands or digest-bound imported results	Evidence quality remains visible
Structural backend	Narrow open Yosys reference backend	Backend pass means no configured finding, not a true negative
Outcomes	pass, fail, unknown, tool_error	Missing intent or coverage never becomes pass
Platforms	Python 3.11–3.13; tested on macOS and Linux	Native Windows is not tested; use Docker Desktop or WSL2

Read the full methodology, limitations, and scope boundary before making claim-bearing use of a profile.

Trust and security boundary

• SV-Gap runs locally and performs no telemetry or artifact uploads. A model generator command supplied by the user may contact its configured provider.
• Generated RTL and functional commands are untrusted input. Do not evaluate them on a workstation containing credentials or sensitive source trees.
• The recommended two-stage workflow generates in the credentialed environment and evaluates saved responses in a network-disabled, read-only container.
• Only open-source runtime tools are assumed by default. Tool versions, provenance, unknowns, and errors remain in the evidence record.
• SV-Gap is evidence infrastructure, not a replacement for organizational review, commercial verification, or signoff.
• GitHub's automatic contributor graph reflects commit authors, including disclosed AI assistance; it is not a roster of verified human researchers. Maintainer accountability and accepted contributions are documented in CONTRIBUTORS.md.

Follow the isolated evaluation recipe for model or contributor outputs you have not reviewed.

Run locally

The container is the shortest reproducible path and includes the open RTL toolchain:

docker run --rm ghcr.io/shsridhar-beep/svgap:v0.3.0-alpha.5 demo

For a native macOS installation:

brew install yosys icarus-verilog
python3 -m venv .venv
.venv/bin/python -m pip install svgap==0.3.0a5
.venv/bin/svgap doctor
.venv/bin/svgap demo

Ubuntu, Debian, CI, and troubleshooting instructions are in Linux installation and doctor checks. If doctor finds a missing prerequisite, it prints the installation command or container fallback rather than leaving the user at a missing-tool report.

Evaluate a model or existing RTL

Any model harness can participate: read a prompt from stdin and write the model response to stdout.

svgap study run reset-release-v0.2 \
  --command "python3 my_generate.py" \
  --label my-model-a \
  --smoke \
  --output my-first-svgap-study

The run produces a portable summary, evidence-file list, reports, and static HTML profile. Replace --smoke with --full for the frozen eight-task, three-sample protocol. See Evaluate your model.

For existing RTL, use svgap init, validate, check, and explain; the bring-your-own-RTL tutorial includes an executable manifest and imported-result path. Python integrations can call svgap.evaluate(manifest); see the Python API.

Current evidence

• Four controlled CDC/RDC witness pairs have identical functional outcomes and different configured structural outcomes.
• A frozen 72-call reset-release study contains 57 functional passes; at least 14 contain the declared raw-reset pattern.
• A heuristic inventory covers 508 public RTL-generation tasks across VerilogEval, RTLLM, and CVDP.
• Two reproducible open-weights profiles demonstrate the public submission path; they are maintainer-produced anchors, not independent replications.

Controlled result · Reset result · Benchmark audit · Evidence profiles · Compact research note

These are bounded existence, taskpack-conditional, and heuristic results. They are not a population defect estimate, general model ranking, or signoff claim.

Collaborate

The preferred entry point is one question a functional RTL evaluation leaves unanswered. A 30-minute scoping call should end with a bounded qualification experiment, explicit claim boundary, and go/revise/stop decision.

• Research-call intake
• Private maintainer email
• Design-partner workflow
• One-page experiment contract
• Replication and co-design discussion

Joining a call is not contributor status. Named credit follows accepted, attributable protocol design, redistributable evidence, task design, analysis, validation, documentation, or code. See Contributors and Contributing.

Extend and integrate

• Submit a result
• Write a checker backend
• Integrate an existing benchmark
• Use the GitHub Action or container
• Good first issues
• Roadmap

Project status and citation

SV-Gap is early research software maintained by Shraddha S, who is accountable for project direction, incorporated changes, research claims, and releases. Material AI development assistance is disclosed in CONTRIBUTORS.md.

SV-Gap is an independent open-source research project. It is not an NVIDIA product or an official statement by NVIDIA. Cite the exact release used. The version-specific alpha.5 archival DOI will be recorded after Zenodo ingests the tag; the all-versions DOI is doi:10.5281/zenodo.21198938.

Apache-2.0. External tools and imported datasets retain their own licenses.