Output-Stationary Deep Dive: Reports and Metrics

Reports and metrics

Reports and Metrics for Output-Stationary Deep Dive is anchored on Partial-sum spill frequency and accumulator energy per output element at target precision modes.. 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 - Output-Stationary Deep Dive

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| 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 Partial-sum spill frequency and accumulator energy per output element at target precision modes. 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

Output-stationary mapping holds partial sums close to compute until reduction completion, minimizing repeated writeback and reload of intermediate values. Activations and weights traverse the array while each PE accumulates into local state, then emits final outputs when tiles close. This can materially improve efficiency for reduction-heavy operators, but only if accumulator sizing, saturation policy, and update timing align with workload statistics. If local accumulation capacity is undersized, spill traffic to shared buffers grows quickly and can dominate both latency and power. A useful explanation always ties observed symptom to a repeatable path where useful work was blocked, delayed, or diluted by overhead.

Use Partial-sum spill frequency and accumulator energy per output element at target precision modes. as an alarm, then anchor action using hard evidence such as Accumulator sizing and spill-risk analysis with precision policy and writeback thresholds..

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 Output-Stationary Deep Dive, reports should explain why Partial-sum spill frequency and accumulator energy per output element at target precision modes. moved and which path consumed budget first.