  Sustaining high fidelity and high throughput of perception tasks over vision
sensor streams on edge devices remains a formidable challenge, especially given
the continuing increase in image sizes (e.g., generated by 4K cameras) and
complexity of DNN models. One promising approach involves criticality-aware
processing, where the computation is directed selectively to critical portions
of individual image frames. We introduce MOSAIC, a novel system for such
criticality-aware concurrent processing of multiple vision sensing streams that
provides a multiplicative increase in the achievable throughput with negligible
loss in perception fidelity. MOSAIC determines critical regions from images
received from multiple vision sensors and spatially bin-packs these regions
using a novel multi-scale Mosaic Across Scales (MoS) tiling strategy into a
single canvas frame, sized such that the edge device can retain sufficiently
high processing throughput. Experimental studies using benchmark datasets for
two tasks, Automatic License Plate Recognition and Drone-based Pedestrian
Detection, show that MOSAIC, executing on a Jetson TX2 edge device, can provide
dramatic gains in the throughput vs. fidelity tradeoff. For instance, for
drone-based pedestrian detection, for a batch size of 4, MOSAIC can pack input
frames from 6 cameras to achieve (a) 4.75x higher throughput (23 FPS per
camera, cumulatively 138FPS) with less than 1% accuracy loss, compared to a
First Come First Serve (FCFS) processing paradigm.

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