  Low-power computer vision on embedded devices has many applications. This
paper describes a low-power technique for the object re-identification (reID)
problem: matching a query image against a gallery of previously seen images.
State-of-the-art techniques rely on large, computationally-intensive Deep
Neural Networks (DNNs). We propose a novel hierarchical DNN architecture that
uses attribute labels in the training dataset to perform efficient object reID.
At each node in the hierarchy, a small DNN identifies a different attribute of
the query image. The small DNN at each leaf node is specialized to re-identify
a subset of the gallery: only the images with the attributes identified along
the path from the root to a leaf. Thus, a query image is re-identified
accurately after processing with a few small DNNs. We compare our method with
state-of-the-art object reID techniques. With a 4% loss in accuracy, our
approach realizes significant resource savings: 74% less memory, 72% fewer
operations, and 67% lower query latency, yielding 65% less energy consumption.
