Hybrid Dataflow Selection: Reports and Metrics

Reports and metrics

Reports and Metrics 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.

A useful report explains why movement happened, not only that movement happened.

Evidence matrix

EVIDENCE MATRIX - Hybrid Dataflow Selection

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| Evidence                    | Tells you                      | Does not prove                 | Next action               |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| occupancy + timeline traces | where utilization is lost      | precise root cause             | map to memory and schedule|
| cache/SRAM/bandwidth stats  | data movement pressure         | model-level quality impact     | correlate with quality run|
| counter + profile alignment | bottleneck class confidence    | rollout safety                 | run full regression matrix|
| thermal/power telemetry     | sustained operating envelope   | correctness closure            | pair with verification    |
| before/after scenario pack  | mitigation movement            | long-tail stability            | execute guardrail replay  |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+

• Track End-to-end joules per inference and latency improvement from per-layer dataflow switching versus single-style baseline. on representative production workloads.

• Include build/runtime metadata in every report header.

• Correlate throughput, latency, and quality before rollout decisions.

• Call out contradictory evidence explicitly.

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.

Report interpretation

Hybrid policies choose different dataflows by operator class or shape regime instead of enforcing one stationary style globally. For example, output-stationary may win on deep reductions while weight-stationary performs better on high-filter-reuse layers, and input-stationary can help bandwidth-bound feature maps. The key is selecting switch points that account for retile overhead, control complexity, and compiler/runtime transition cost. Teams usually rely on profiling-guided heuristics or cost models that include both kernel efficiency and orchestration penalty. A useful explanation always ties observed symptom to a repeatable path where useful work was blocked, delayed, or diluted by overhead.

Use End-to-end joules per inference and latency improvement from per-layer dataflow switching versus single-style baseline. as an alarm, then anchor action using hard evidence such as Dataflow policy playbook with layer-wise recommendations, transition rules, and expected gains..

Dataflow selection governs reuse, movement cost, and predictability across model shapes. Senior reviews expect a chain of proof: workload intent -> mapping -> hardware behavior -> product impact.

For Hybrid Dataflow Selection, reports should explain why End-to-end joules per inference and latency improvement from per-layer dataflow switching versus single-style baseline. moved and which path consumed budget first.