LPV Regression: Design Space

Design space exploration

For LPV Regression, teams balance safety, closure speed, and simulation or debug cost.

Option A - conservative

• Conservative controls: helps strong safety and clarity

• Risk: higher setup and runtime overhead

• Validate with: new LPV program bring-up

Option B - balanced

• Balanced controls: helps good closure velocity

• Risk: needs disciplined review

• Validate with: shared platform verification

Option C - aggressive

• Aggressive optimization: helps lower overhead

• Risk: higher corner-case risk

• Validate with: mature flows with strong telemetry

Option D - refactor

• Refactor path: helps long-term robustness

• Risk: migration cost

• Validate with: legacy LPV debt cleanup

DESIGN SPACE - LPV Regression
coverage depth <-> simulation/runtime cost <-> debug clarity <-> residual risk

Design pitfalls

• Optimizing runtime before proving intent-correctness boundaries.

• Adding checks without ownership of closure and triage workflow.

Low-power verification deep dive

Signoff confidence comes from triage discipline, reproducible proof, and explicit residual-risk decisions.

Concept diagram

LPV SIGNOFF LADDER

reproduce -> classify -> isolate boundary -> bounded fix -> replay -> signoff decision

Metric graph

SIGNOFF CONFIDENCE

open ambiguous failures  ██████
reproducible closures    ███████
residual-risk unknowns   ███

Metrics and artifacts to collect

• X-prop triage classification report

• bug root-cause closure packet

• regression stability and recurrence trend

• signoff checklist completion matrix

Mini case study

A signoff block cleared after the team replaced broad waivers with boundary-specific evidence and replay criteria.

Debug branches

• Classify X behavior before broad waiving.

• Capture one definitive artifact packet per closure claim.

• Define residual risk and rollback path at signoff.

Senior review question

Ask: what exact low-power transition boundary failed first, and which artifact proves the closure claim reproducibly?

Key takeaways

• Tie each LPV claim to a concrete transition boundary and one proving artifact.

• Prefer minimal reversible fixes with explicit owner and rollback criteria.

Common pitfalls

• Treating power-aware failures as random before boundary classification.

• Waiving X-prop failures before proving impact and root cause.

• Declaring closure without deterministic replay across key modes.

Principal LPV review addendum

LPV Regression should be reviewed as a transition integrity system, not just isolated checks.

Use illegal transition count, corruption incidence, and reproducibility of low-power regressions across fixed seeds as alarm and LPV evidence packet: transition timeline, assertion outcomes, and before-after replay summary as proof.

LPV debug and signoff require disciplined triage: classify X behavior, isolate root cause, and close with reproducible evidence. Closure quality comes from reproducible evidence and explicit owners.