Weight-Stationary Dataflow: Inputs and Outputs

Inputs and outputs contract

Inputs and Outputs for Weight-Stationary Dataflow is anchored on Weight reuse factor and off-array weight bandwidth per tera-operations for representative inference layers.. Convert measurements into mechanism-backed decisions with clear owner accountability.

INPUTS
  - workload profile and SLA target
  - model precision and quality thresholds
  - compiler/runtime/firmware metadata
  - hardware operating envelope assumptions

OUTPUTS
  - evidence-backed bottleneck classification
  - owner-signed mitigation proposal
  - validation matrix with rollback triggers
  - release recommendation

Ownership split

OWNERSHIP LAYERS - Weight-Stationary Dataflow

+----------------------+--------------------------------+--------------------------------+
| Team                 | Primary responsibility         | Closure artifact               |
+----------------------+--------------------------------+--------------------------------+
| accelerator architect | mechanism and architecture intent| design rationale + tradeoffs   |
| compiler mapping owner | mapping, runtime, and execution   | profile traces + bottleneck map|
| SRAM subsystem owner | correctness, risk, and signoff    | test report + closure memo     |
+----------------------+--------------------------------+--------------------------------+

AI accelerator deep dive

Systolic efficiency is governed by feed quality, tile fit, and bubble control.

Concept diagram

SYSTOLIC WAVEFLOW

operand stream -> wavefront launch -> PE mesh compute -> reduction/writeback
                         ^ bubbles and feed stalls reduce realized throughput

Metric graph

UTILIZATION LOSSES

tile mismatch      ███████
feed stalls        █████████
sync bubbles       █████

Metrics and artifacts to collect

• mesh occupancy timeline

• fill-drain overhead

• tile mismatch histogram

• DRAM stall attribution

Mini case study

Increasing mesh size did not help until scheduling and tile alignment removed persistent wavefront bubbles.

Debug branches

• Measure bubble source first

• Classify compute vs memory starvation

• Tune tile policy before frequency changes

Senior review question

Ask: which first-principles bottleneck class explains the symptom, and what artifact proves it reproducibly?

Key takeaways

• Tie every accelerator claim to a reproducible workload slice and one primary metric trend.

• Prefer bounded fixes with clear owner and rollback boundary over broad tuning bundles.

Common pitfalls

• Optimizing synthetic kernels without production-shape validation.

• Reading average latency while ignoring p95 and p99 behavior.

• Declaring sparse or precision wins without fallback and quality evidence.

Handoff explanation

Inputs should include workload profile, model revision, compiler/runtime versions, and platform power mode.

Outputs must include actionable interpretation of Weight reuse factor and off-array weight bandwidth per tera-operations for representative inference layers., required artifacts (Dataflow mapping report showing weight residency, activation traffic, and buffer pressure by layer type.), owner, and validation scope.

The ideal handoff packet is reproducible: fixed seeds, explicit baseline, and rejected alternatives.