Output-Stationary Dataflow: Review Checklist

Review checklist

Review Checklist for Output-Stationary Dataflow is anchored on Partial-sum spill rate and accumulator energy per output element across matrix and convolution workloads.. Convert measurements into mechanism-backed decisions with clear owner accountability.

• Workload scope and SLA targets are explicit.

• Environment metadata is locked and reproducible.

• Mechanism classification is evidence-backed.

• Owner, rollback trigger, and validation matrix are documented.

• Owners signed: microarchitecture owner, numeric precision owner, compiler mapping owner, verification owner, post-silicon performance owner.

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.

Review checklist explanation

Checklist quality determines whether teams close on proof or on optimism.

Minimum packet: metric trend (Partial-sum spill rate and accumulator energy per output element across matrix and convolution workloads.), artifact set (Accumulator sizing and spill analysis with precision budget and writeback frequency breakdown.), bottleneck class, owner fix, rollback trigger, and validation matrix.

If precision changes are involved, include quality guardrail evidence for each deployment slice.