Physical-Aware Debug: Mechanism

Mechanism to understand

Mechanism for Physical-Aware Debug focuses on logic-vs-physical delta isolation time, root-cause class distribution. The goal is to connect observed QoR movement to mechanism, ownership, and regression risk.

Physical-aware debug classifies QoR regressions into modeling error, placement pressure, or true logic issues before ECO churn starts. Treat synthesis as a contract: constraints + transforms + reports must agree.

• Identify which lane moved first: timing, area, power, or runtime.

• Identify whether drift is input, transform, or correlation related.

• Identify owner before applying any ECO.

Flow model

SYNTHESIS FLOW — Physical-Aware Debug

RTL + constraints
      |
      v
elaboration + checks
      |
      v
mapping + optimization
      |
      v
QoR reports (timing/area/power)
      |
      v
incremental ECO + regression

Primary metric: logic-vs-physical delta isolation time, root-cause class distribution

Logic vs physical delta

compile QoR good, route QoR bad?
  -> model mismatch
  -> congestion issue
  -> true logic bottleneck

Layer responsibilities

SYNTHESIS OWNERSHIP LAYERS — Physical-Aware Debug

layer               owns                          failure mode
----------------    ---------------------------   -------------------------
constraints         clocks/exceptions/policy      fake QoR optimism
mapping             cell choices/structure        depth/fanout regressions
optimization        timing/power tradeoffs        one-metric overfitting
physical-aware      topo/congestion estimates     handoff delta surprises
closure             ECO order/regression          fixes break other corners

Synthesis deep dive

Physical-aware synthesis reduces logic-to-route surprise when correlation is monitored.

Concept diagram

PHYSICAL-AWARE LOOP

topo compile -> congestion hints -> handoff -> PD correlation

Metric graph

CORRELATION DELTA

logic view WNS      ███████
topo view WNS       █████
route trial WNS     ████

Reports and artifacts

• topo QoR

• congestion heatmap

• handoff manifest

• delta compare vs PD

Mini case study

Topo compile looked clean, but hotspot congestion predicted route failure and prevented late ECO churn.

Debug branches

• Check hotspot ownership

• Bound correlation drift

• Escalate if handoff assumptions stale

Senior review question

Ask: what evidence proves this QoR move is real and stable?

Key takeaways

• State exact run context (RTL, SDC, libs, switches) with every QoR claim.

• Re-run timing, area, and power regressions after each synthesis ECO.

Common pitfalls

• Comparing runs with mismatched constraints or library views.

• Timing-only fixes that violate power or area budgets.

• Skipping equivalence checks after structural changes.

Mechanism deep dive

Physical-aware debug classifies QoR regressions into modeling error, placement pressure, or true logic issues before ECO churn starts.