LPV Regression

What this topic teaches

LPV Regression converts LPV concepts into staff-level verification decisions. LPV regression strategy combines static and dynamic verification so coverage reflects both intent correctness and temporal behavior under stress. Static UPF checks should gate every change for missing isolation, illegal level-shifter topology, inconsistent retention definitions, unconnected supplies, and PST rule violations before simulation capacity is consumed. Dynamic regression then exercises transition-rich scenarios with constrained-random concurrency, software-like sequencing, and assertion monitors for power-safe protocol operation during shutoff and wake-up. Mature flows run differential buckets such as full-power baseline, power-aware strict semantics, and targeted pessimism/X-prop suites to detect masking effects. Closure requires trend tracking on flaky low-power tests, deterministic replay for rare failures, and explicit mapping from high-risk power scenarios to nightly and pre-release gates.

Senior-engineer framing question

When illegal transition rate, corruption incidence, and deterministic replay quality under low-power scenarios regresses, can you isolate first failing low-power boundary, prove it with artifacts, assign owners, and close with rollback-safe validation?

LOW-POWER VERIFICATION FLOW - LPV Regression

power intent and mode definitions
      |
      v
domain controls and transition sequencing
      |
      v
simulation behavior (isolation, retention, corruption)
      |
      v
assertions and coverage evidence
      |
      v
triage, bounded fix, and signoff closure

Evidence to collect

• Primary metric: illegal transition rate, corruption incidence, and deterministic replay quality under low-power scenarios.

• Primary artifact: evidence packet for LPV Regression: transition timeline, assertions, and before-after replay summary.

• Owners to include: LPV lead, power-intent owner, LPV Debug & Signoff owner.

• One reproducible failing scenario and one stable comparator run.

• One fixed metadata run with branch and configuration tags locked.

Ownership layers

OWNERSHIP LAYERS - LPV Regression

+----------------------+--------------------------------+--------------------------------+
| Team                 | Primary responsibility         | Closure artifact               |
+----------------------+--------------------------------+--------------------------------+
| LPV lead | scenario intent and closure      | review rationale memo          |
| power-intent owner | transition and boundary contract | timeline + assertion packet    |
| LPV Debug & Signoff owner | regression signoff readiness     | validation matrix + risk note  |
+----------------------+--------------------------------+--------------------------------+

Decision matrix

EVIDENCE MATRIX - LPV Regression

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| Evidence                    | Tells you                      | Does not prove                 | Next action               |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| transition timeline traces  | first failing LP phase         | complete root-cause ownership  | correlate with intent map |
| UPF-aware assertion logs    | contract violations by phase   | silicon product impact         | map to scenario severity  |
| corruption/X classification | actionable vs noisy failures   | legal transition completeness  | replay key mode corners   |
| save/restore snapshots      | state integrity movement       | isolation correctness          | pair with crossing checks |
| before-after regressions    | mitigation movement quality    | long-tail stability            | run full matrix           |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+

Key takeaways

• Start with transition-boundary classification before broad methodology changes.

• Tie each LPV claim to one proving artifact and one owner action.

• Close with validation matrix and rollback trigger for signoff safety.

Common pitfalls

• Waiving failures before first-failure boundary classification.

• Changing intent, RTL, and checkers in one step and losing causality.

• Declaring closure on local runs without broader replay coverage.

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.