Low Power Verification · All levels

How LPV Differs from Functional Verification: Debug Playbook

Debug Playbook for How LPV Differs from Functional Verification.

Debug playbook

Debug Playbook for How LPV Differs from Functional Verification is anchored on illegal transition rate, corruption incidence, and deterministic replay quality under low-power scenarios. Convert observations into mechanism-backed and owner-bound actions.

Freeze seed, metadata, and boundary under investigation.
Locate first persistent low-power phase divergence.
Classify mechanism: setup, transition, boundary, retention, or X-prop class.
Apply one focused reproducer and one bounded fix.
Re-run determinism and broader regression matrix.

Review memo template

diagram

LPV REVIEW MEMO - Low Power Verification Foundations / How LPV Differs from Functional Verification

1. Symptom
   - Failing metric: illegal transition rate, corruption incidence, and deterministic replay quality under low-power scenarios
   - Trigger context: <seed/mode/sequence>
   - First failing phase: <entry/off/exit/boundary>

2. Mechanism hypothesis
   - Candidate mechanism: Functional verification asks whether logic behavior matches the architectural spec under valid operating assumptions, while LPV asks whether logic remains safe and correct as operating assumptions themselves change with power state. LPV introduces failure modes that functional-only regressions often miss: unknown propagation after shutoff, incorrect isolation sequencing, lost state without retention, protocol violations across partially powered systems, and invalid resets around domain wake-up. Effective teams integrate LPV scenarios into existing testbenches but add power-state-aware stimulus, checkers, and coverage models that explicitly measure state transitions, low-power handshakes, and recovery correctness.
   - Competing hypotheses: setup, transition race, boundary bug, retention drift, X-prop noise
   - Missing evidence: <trace/assertion/report>

3. Proposed action
   - Smallest reversible change: <intent/RTL/checker/flow>
   - Expected movement: <failure trend/replay stability>
   - Regression risk: compatibility, coverage, signoff delay

4. Signoff
   - Required artifact: evidence packet for How LPV Differs from Functional Verification: transition timeline, assertions, and before-after replay summary
   - Required owners: LPV lead, power-intent owner, Low Power Verification Foundations owner
   - Final decision: ship, bounded rollout, rollback, or escalate

Low-power verification deep dive

LPV foundations are strongest when power intent, simulation semantics, and ownership boundaries are explicit from day one.

Concept diagram

diagram

LPV FOUNDATION LOOP

intent definition -> setup and modeling -> scenario execution -> evidence-based closure
       ^                                                              |
       +------------------------ owner feedback ----------------------+

Metric graph

diagram

FOUNDATION HEALTH

setup escapes             █████
intent mismatch defects   ██████
stable regressions        █████████

Metrics and artifacts to collect

intent-to-RTL alignment checklist
power-mode onboarding packet
ownership map for controls and checks
first-failure boundary report

Mini case study

A project reduced LPV bring-up churn after requiring explicit domain-control ownership and transition evidence in every review.

Debug branches

Prove setup correctness before chasing downstream symptoms.
Record domain ownership for each control and checker.
Distinguish intent mismatch from RTL implementation bugs.

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.

Debug ladder

Sequence: reproduce -> classify -> isolate boundary -> prove mechanism -> bounded fix.

Avoid mixed fixes before first-principles classification.

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