Datapath Inference: Design Space

Design space exploration

For Datapath Inference, options trade QoR gain, risk, and schedule.

Option A — conservative

• Conservative constraints: helps predictability

• Risk: slower PPA gains

• Validate with: first-silicon safety

Option B — balanced

• Balanced QoR tuning: helps stable closure

• Risk: requires discipline

• Validate with: typical product mode

Option C — aggressive compile

• Aggressive compile push: helps fast timing recovery

• Risk: power/hold regressions

• Validate with: late schedule

Option D — structural change

• Structural RTL change: helps long-term fix

• Risk: schedule impact

• Validate with: recurring bottleneck

DESIGN SPACE — Datapath Inference
QoR gain <-> regression risk <-> schedule pressure

Design pitfalls

• Single-metric optimization

• No rollback path

• Weak run comparability

Tradeoff curve

BEFORE / AFTER QOR — Datapath Inference

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

Validate timing + area + power, not one number.

Synthesis deep dive

Datapath and retiming gains are only real if formal and timing remain clean.

Concept diagram

DATAPATH + RETIME

operator inference -> stage balancing -> formal proof -> QoR validation

Metric graph

FREQUENCY VS LATENCY

Fmax gain   ████████
latency cost ████

Reports and artifacts

• retiming report

• datapath inference log

• equivalence status

• latency impact sheet

Mini case study

Retiming met frequency target, but missing formal hooks delayed closure by two days.

Debug branches

• Check retime blockers

• Formal first for aggressive moves

• Compare with pipeline option

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

Datapath-aware synthesis recognizes arithmetic patterns and maps them to optimized structures; RTL shape determines whether inference succeeds.

Metric: inferred arithmetic structure count, depth reduction, datapath area delta