Dataflow Architectures: Tricky Q&A

[INT][ACCEL][DATAFLOW-ARCHITECTURES]

Q: When does input-stationary dataflow provide the biggest benefit?

A:
It performs best when activation movement dominates memory energy, such as workloads with high feature-map reuse across many MAC operations. Keeping inputs local can cut repeated fetches from upper memory levels, but only if weights and reductions are scheduled without introducing stalls. Benefit should be validated with traffic breakdown, not assumed from the label.

FOLLOW-UP TRAP: Choosing input-stationary without confirming activation traffic is the dominant cost.

[INT][ACCEL][DATAFLOW-ARCHITECTURES]

Q: Why can output-stationary lose its advantage in production?

A:
Its main gain comes from local partial-sum accumulation, but undersized accumulators or misaligned timing force frequent spills to shared memory. Once spill traffic rises, latency and energy can degrade quickly and erase theoretical savings. Accurate sizing requires workload-aware reduction-depth and precision analysis.

FOLLOW-UP TRAP: Assuming output-stationary is always superior for any reduction-heavy layer.

[INT][ACCEL][DATAFLOW-ARCHITECTURES]

Q: What is the practical risk in weight-stationary execution?

A:
Stationary weights reduce parameter fetch cost, but activation delivery and partial-sum transport can become the new bottleneck if the feeder network is underprovisioned. In that case, PEs hold weights yet idle waiting for inputs. Evaluating reuse alongside interconnect and buffer pressure is essential.

FOLLOW-UP TRAP: Optimizing for weight reuse alone while ignoring activation and reduction bandwidth limits.

[INT][ACCEL][DATAFLOW-ARCHITECTURES]

Q: How should teams decide whether to deploy a hybrid dataflow policy?

A:
Use layer-level profiling and a cost model that includes both kernel efficiency and switching overhead between dataflows. Hybrid wins when per-layer gains exceed orchestration, retile, and control complexity costs across the full model path. The decision should be made from end-to-end latency and energy evidence, not isolated kernel benchmarks.

FOLLOW-UP TRAP: Mixing dataflows by intuition without accounting for transition overhead.

SYMPTOM -> BOTTLENECK CLASS -> ARTIFACT -> OWNER ACTION -> VALIDATION

DATAFLOW DECISION

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