Good point. Reusing the same clip controls the scene content, but stream timing could still change how much classifier work arrives at once. We haven’t measured that alignment, so I wouldn’t assume the streams were either synchronized or independent.
Comparing staggered playback with a synchronized crowd burst would help separate average demand from burst pressure. I’d track the crops reaching the classifier and queue depth alongside the FPS floor, to see whether similar average rates hide different backlogs.
That would be a useful extension of this benchmark. Thanks for calling it out.
Replaying the same fifteen-second clip on every channel makes the load reproducible, but it can also synchronize the expensive parts of the workload. For the helmet classifier, all eight streams may reach their busiest target count at the same time.
I would repeat the staircase with phase offsets between streams and with a deliberately synchronized crowd burst, retaining crop counts alongside the FPS floor. Those two runs would show whether capacity depends on aggregate average demand or short classifier bursts. That complements your distinction between task-frame updates and accelerator invocations without treating eight passing channels as a universal hardware limit.