AI Accelerator Design · All levels

Scheduling & Workload Mapping

Execution planning for modern accelerators: fusion and tiling choices, batch policy tradeoffs, multi-core dispatch strategy, and compiler-runtime coordination.

Section goal

Execution planning for modern accelerators: fusion and tiling choices, batch policy tradeoffs, multi-core dispatch strategy, and compiler-runtime coordination.

How to study this section

Start with each topic hub and restate the mechanism in your own words.
Use reports and debug pages to separate symptoms from root causes.
Practice worked examples and interview drills under fixed metadata.
Close with checklist and silicon impact before making release claims.

Topics

operator-fusion-and-tiling/ - Operator Fusion and Tiling
batch-size-effects/ - Batch Size Effects
multi-core-accelerator-scheduling/ - Multi-Core Accelerator Scheduling
compiler-runtime-mapping/ - Compiler-Runtime Mapping

AI accelerator deep dive

Scheduling quality decides whether architecture headroom reaches product throughput.

Concept diagram

diagram

SCHEDULING PIPELINE

compile plan -> runtime queue -> core placement -> completion and tail behavior

Metric graph

diagram

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?

Practice this lesson

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