Power Bug Triage: Theory Deep Dive

Foundational theory

Power Bug Triage is core to LPV Debug & Signoff. Treat each power behavior change as a correctness and signoff risk decision.

Core concepts explained

• Low-power bug triage must start from first power-intent divergence, not the final scoreboard mismatch, because symptoms can appear thousands of cycles after the causative transition. Practical triage stacks evidence in layers: power controller command and acknowledge timeline, domain/supply state traces, isolation and retention control behavior, then protocol and functional fallout. Classifying defects early into sequencing, intent-binding, structural insertion, or software orchestration issues avoids expensive cross-team ping-pong and speeds owner routing. Strong teams maintain reproducible minimal tests for each bug class and track escape patterns such as intermittent wake-up corruption, stale retained state, or cross-domain deadlock that only occurs under concurrent traffic and power events.

• Primary metric: illegal transition count, corruption incidence, and reproducibility of low-power regressions across fixed seeds

• Primary artifact: LPV evidence packet: transition timeline, assertion outcomes, and before-after replay summary

• Owners: LPV owner, PMU or firmware owner, verification signoff owner

• Power intent and RTL behavior must stay aligned through transitions

• Proof quality beats broad waive strategies in low-power closure

Why this matters in low-power signoff

LPV debug and signoff require disciplined triage: classify X behavior, isolate root cause, and close with reproducible evidence. Teams that enforce this reduce false alarms and real escapes.

Mental model

ROOT CAUSE TREE

Observed mismatch in low-power test
             |
      Reproducible?
        /      \
      no        yes
      |          |
 test instability   classify failing phase
                   /       |        \
               entry    off-state   exit
                 |         |         |
             sequence   iso/ret   restore/deiso
                 |         |         |
              instrument + bisect + confirm

Worked intuition

1. Classify symptom first: illegal transition, corruption, isolation break, retention drift, or X-prop ambiguity.

2. Pinpoint first phase boundary where expected low-power behavior diverges.

3. Quantify movement in illegal transition count, corruption incidence, and reproducibility of low-power regressions across fixed seeds before broad refactors.

4. Collect LPV evidence packet: transition timeline, assertion outcomes, and before-after replay summary with fixed run metadata and mode sequencing.

5. Apply one bounded fix and replay both targeted and broader scenarios.

6. Publish owner-signed closure note with rollback trigger.

Common misconceptions

• Passing nominal ON/OFF smoke proves transition correctness.

• UPF compile clean means all intent semantics are correct.

• All X-prop failures indicate real product escapes.

• Retention behavior can be trusted without multi-cycle restore stress.

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.

Theory reinforcement

Theory matters when it predicts concrete failure signatures and closure boundaries.

Translate LPV semantics into reproducible verification outcomes.