Datapath Inference: Interview Drills

Interview drills

Interview Drills 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.

PROMPT
You observe inferred arithmetic structure count, depth reduction, datapath area delta in Datapath Inference. Walk through root cause and closure plan.

STRONG ANSWER
1. Names run context and affected QoR lane.
2. Explains Datapath-aware synthesis recognizes arithmetic patterns and maps them to optimized structures; RTL shape determines whether inference succeeds.
3. Requests datapath extraction report, inferred operator map, QoR comparison.
4. Proposes one reversible fix and full regression scope.

WEAK ANSWER
Jumps to random tool switches without mechanism evidence.

Whiteboard diagram

Datapath extraction

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

Root-cause narration

ROOT-CAUSE TREE — Datapath Inference

inferred arithmetic structure count, depth reduction, datapath area delta regressed
        |
   same RTL/constraints tag?
     /              \
   no                yes
   |                  |
input drift      transform side-effect
 /    \             /         \
SDC    libs      mapping      physical estimate
diff   diff      choice       mismatch

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