  Modern retrospective analytics systems leverage cascade architecture to
mitigate bottleneck for computing deep neural networks (DNNs). However, the
existing cascades suffer two limitations: (1) decoding bottleneck is either
neglected or circumvented, paying significant compute and storage cost for
pre-processing; and (2) the systems are specialized for temporal queries and
lack spatial query support. This paper presents CoVA, a novel cascade
architecture that splits the cascade computation between compressed domain and
pixel domain to address the decoding bottleneck, supporting both temporal and
spatial queries. CoVA cascades analysis into three major stages where the first
two stages are performed in compressed domain while the last one in pixel
domain. First, CoVA detects occurrences of moving objects (called blobs) over a
set of compressed frames (called tracks). Then, using the track results, CoVA
prudently selects a minimal set of frames to obtain the label information and
only decode them to compute the full DNNs, alleviating the decoding bottleneck.
Lastly, CoVA associates tracks with labels to produce the final analysis
results on which users can process both temporal and spatial queries. Our
experiments demonstrate that CoVA offers 4.8x throughput improvement over
modern cascade systems, while imposing modest accuracy loss.
