Datapath Inference: Mechanism

Mechanism to understand

Mechanism 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.

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

Layer responsibilities

SYNTHESIS OWNERSHIP LAYERS — Datapath Inference

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

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.

Mechanism deep dive

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