An accelerator pays off when regular reuse outweighs launch and data-movement cost. This part links pattern choice, dataflow, tiling, and integration contracts to that tradeoff.
Topic | Lessons | Description | Time |
|---|---|---|---|
6 | When an accelerator pays off, how dataflow and locality set efficiency, and what integration must guarantee. | 50 min |
Compute-idle windows are correlated with transfer stalls, queue starvation, and phase synchronization events. Dataflow sets movement and synchronization overhead, which often caps effective throughput before clock rate matters. The Accelerators for the Architect topic covers that link.