Isolation Behavior in Simulation: Reports and Metrics

Reports and metrics

Reports and Metrics for Isolation Behavior in Simulation 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.

A useful report explains why behavior moved, not only that behavior moved.

Evidence matrix

EVIDENCE MATRIX - Isolation Behavior in Simulation

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| 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           |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+

• Track illegal transition rate, corruption incidence, and deterministic replay quality under low-power scenarios on representative low-power scenarios.

• Include branch, seed, mode, and configuration metadata in every report.

• Correlate observed symptoms with phase and boundary assumptions.

• Call out contradictory evidence explicitly.

Low-power verification deep dive

Power-aware simulation quality is measured by realistic transition behavior and actionable failure classification.

Concept diagram

POWER-AWARE SIM FLOW

UPF + RTL + testbench -> elaboration -> transition simulation -> assertions and triage

Metric graph

SIM QUALITY SIGNALS

false-fail noise      █████
actionable failures   ███████
deterministic replay  ████████

Metrics and artifacts to collect

• elaboration semantic report

• power-aware run reproducibility matrix

• corruption and clamp behavior summary

• assertion signal-to-noise trend

Mini case study

A noisy regression became actionable after bucketing failures by transition phase and boundary type before fixing checks.

Debug branches

• Start from first failing phase, not final mismatch.

• Check semantic setup consistency before declaring design bug.

• Use one reproducible scenario per hypothesis branch.

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.

Report interpretation

Read metric movement with phase and boundary context.

A report is actionable only when it isolates first failing transition boundary.