Hybrid Dataflow Selection: Review Checklist

Review checklist

Review Checklist for Hybrid Dataflow Selection is anchored on End-to-end joules per inference and latency improvement from per-layer dataflow switching versus single-style baseline.. 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: system architect, compiler/runtime owner, performance modeling owner, production inference lead.

AI accelerator deep dive

Dataflow choices are durable architecture decisions that shape memory and scheduling cost.

Concept diagram

DATAFLOW DECISION

input/output/weight stationary
  -> locality pattern
  -> movement cost
  -> throughput and power

Metric graph

DATAFLOW COST MIX

activation traffic   ███████
weight traffic       █████
partial-sum traffic  ██████

Metrics and artifacts to collect

• reuse factor map

• buffer pressure profile

• NoC traffic mix

• shape sensitivity analysis

Mini case study

A dataflow that won for convolution lost on attention-heavy batches due to activation movement pressure.

Debug branches

• Segment by model family

• Compare reuse vs movement

• Re-check mapping assumptions under batch variance

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 (End-to-end joules per inference and latency improvement from per-layer dataflow switching versus single-style baseline.), artifact set (Dataflow policy playbook with layer-wise recommendations, transition rules, and expected gains.), bottleneck class, owner fix, rollback trigger, and validation matrix.

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