Datapath Inference: Worked Example

Worked example

Worked Example for Datapath Inference focuses on inferred arithmetic structure count, depth reduction, datapath area delta. The goal is to connect observed QoR movement to mechanism, ownership, and regression risk.

A weekly compile reports inferred arithmetic structure count, depth reduction, datapath area delta regression. Instead of random knob tuning, start by proving whether input context changed and which mechanism dominates.

Sequence under inspection

SYNTHESIS FLOW — Datapath Inference

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

Primary metric: inferred arithmetic structure count, depth reduction, datapath area delta

Datapath extraction

RTL arithmetic pattern
  -> operator recognition
  -> datapath macro mapping
  -> depth and area reduction

1. Capture baseline and regressed artifact pair.

2. Confirm manifest parity (RTL, SDC, libs, switches).

3. Pin first metric drift and mechanism hypothesis.

4. Reproduce with datapath extraction report, inferred operator map, QoR comparison.

5. Apply one reversible fix and run matrix regression.

Did the fix hold?

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