AI Accelerator Design · All levels
Compiler-Runtime Mapping: Review Checklist
Review Checklist for Compiler-Runtime Mapping.
Review checklist
Review Checklist for Compiler-Runtime Mapping is anchored on Compile-time mapping accuracy versus runtime adaptation gain for changing input shapes.. 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: compiler lead, runtime lead, observability engineer, model serving architect.
AI accelerator deep dive
Scheduling quality decides whether architecture headroom reaches product throughput.
Concept diagram
SCHEDULING PIPELINE
compile plan -> runtime queue -> core placement -> completion and tail behaviorMetric graph
TAIL-LATENCY DRIVERS
queueing delay ███████
core imbalance █████
mapping fallback ████Metrics and artifacts to collect
queue wait profile
batch policy impact
core-level fairness
operator fusion effect
Mini case study
Aggressive fusion reduced launch overhead but increased memory bursts that worsened p95 latency.
Debug branches
Inspect tail first, not average
Check fairness across streams
Validate fusion against memory constraints
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 (Compile-time mapping accuracy versus runtime adaptation gain for changing input shapes.), artifact set (Closed-loop mapping report combining offline compile profiles with runtime telemetry and retuning actions.), bottleneck class, owner fix, rollback trigger, and validation matrix.
If precision changes are involved, include quality guardrail evidence for each deployment slice.