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
PRECISION-POWER LOOP
numeric format choice -> throughput and energy
+ thermal state and DVFS policy -> sustained SLAMetric graph
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.