PPA Tradeoff Playbook: Silicon PPA Impact

Silicon PPA and closure impact

PPA choices shape leakage budgets, clock power, and physical congestion.

Area drivers

• Buffer growth

• Cell mix shifts

• Retiming duplication

Power drivers

• Leakage from LVT

• Clock power from gating/skew choices

Timing impact

• Critical path depth changes

• Hold/setup coupling

PD consequences

• Congestion ripple

• Handoff predictability

Verification burden

• Equivalence burden

• Regression matrix depth

PPA SNAPSHOT — PPA Tradeoff Playbook
area / dynamic / leakage / WNS / runtime

PPA takeaways

• QoR wins must survive signoff checks

• Correlation beats one-off gains

QoR snapshot

BEFORE / AFTER QOR — PPA Tradeoff Playbook

QoR score
  0 |                     --- target zone
 -1 |        ● regression
 -2 |             ● baseline
 -0.5|                  ● after fix
    +----------------------------------> iteration

Validate timing + area + power, not one number.

Synthesis deep dive

PPA closure is a constrained tradeoff problem, not a timing-only exercise.

Concept diagram

PPA TRIAD

timing target <-> power budget <-> area cap

Metric graph

PARETO PROGRESSION

early runs      o o
balanced point    o
over-tuned          o (risk)

Reports and artifacts

• VT mix

• leakage trend

• dynamic power trend

• PPA Pareto table

Mini case study

LVT-heavy fix recovered setup but violated leakage target; balanced VT strategy closed both.

Debug branches

• Timing-only claims need power check

• Validate hold on VT swaps

• Use Pareto memo

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.

Principal synthesis review addendum

PPA closure is a constrained optimization problem: teams choose options by Pareto quality, not single-metric heroics.

Metric: area-power-timing Pareto points, closure runtime, regression stability