Low Power Verification · All levels

LPV Regression: Debug Playbook

Debug Playbook for LPV Regression.

Debug playbook

Debug Playbook for LPV Regression 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 - LPV Debug & Signoff / LPV Regression

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: 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.
   - 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 LPV Regression: transition timeline, assertions, and before-after replay summary
   - Required owners: LPV lead, power-intent owner, LPV Debug & Signoff owner
   - Final decision: ship, bounded rollout, rollback, or escalate

Low-power verification deep dive

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

Concept diagram

diagram

LPV SIGNOFF LADDER

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

Metric graph

diagram

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.

Debug ladder

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

Avoid mixed fixes before first-principles classification.

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