AI Accelerator Design · All levels

Performance-per-Watt Optimization Workflow: Review Checklist

Review Checklist for Performance-per-Watt Optimization Workflow.

Review checklist

Review Checklist for Performance-per-Watt Optimization Workflow is anchored on Sustained throughput per watt under SLA-constrained load, including memory and host-orchestration overhead.. 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: systems performance engineer, runtime scheduler owner, power modeling owner, capacity planning owner.

AI accelerator deep dive

Precision and DVFS policy must be co-designed with quality guardrails and thermal behavior.

Concept diagram

diagram
PRECISION-POWER LOOP

numeric format choice -> throughput and energy
         + thermal state and DVFS policy -> sustained SLA

Metric graph

diagram
PERF/W TRADE

INT8 efficiency      █████████
BF16 stability       ██████
thermal clamp risk   ████

Metrics and artifacts to collect

  • precision-mode mix

  • perf-per-watt trend

  • thermal clamp frequency

  • quality regression monitor

Mini case study

Switching to lower precision improved nominal throughput, but thermal clamp cycles reduced sustained gains.

Debug branches

  • Validate quality guardrails by slice

  • Correlate thermal events to latency tails

  • Audit precision fallback behavior

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 (Sustained throughput per watt under SLA-constrained load, including memory and host-orchestration overhead.), artifact set (Perf-per-watt optimization report with bottleneck ranking, intervention plan, and before-after operating points.), bottleneck class, owner fix, rollback trigger, and validation matrix.

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