Input-Stationary Dataflow: Comparison Matrix

Comparison matrix

Comparison Matrix for Input-Stationary Dataflow is anchored on Activation reuse factor and input-buffer read traffic per tera-operations for representative convolution and GEMM layers.. Convert measurements into mechanism-backed decisions with clear owner accountability.

EVIDENCE MATRIX - Input-Stationary Dataflow

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| 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  |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+

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.

Principal accelerator review addendum

Input-Stationary Dataflow should be framed as a full-system behavior, not an isolated kernel trick. Production outcomes are set by model shape mix, compiler choices, runtime queueing policy, memory hierarchy limits, and silicon delivery margins.

Input-stationary execution keeps activation tiles resident in local buffers or PE-adjacent storage while weights and partial sums stream through the compute fabric. This reduces repeated activation fetches from higher memory levels, which is valuable when feature-map bandwidth dominates energy. The design challenge is balancing local activation capacity with timely weight delivery and reduction routing so compute units remain occupied. Practical schedulers tune tile shape, preload depth, and multicast strategy to prevent contention that can erase expected reuse gains. A useful explanation always ties observed symptom to a repeatable path where useful work was blocked, delayed, or diluted by overhead.

Use Activation reuse factor and input-buffer read traffic per tera-operations for representative convolution and GEMM layers. as an alarm, then anchor action using hard evidence such as Traffic decomposition sheet showing activation residency, refill cadence, and bottleneck attribution by layer..

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.

Use this addendum to force explicit owner assignment, bounded fixes, and reproducible evidence before declaring closure.