1 research outputs found
Energy-Efficient Object Detection using Semantic Decomposition
Machine-learning algorithms offer immense possibilities in the development of
several cognitive applications. In fact, large scale machine-learning
classifiers now represent the state-of-the-art in a wide range of object
detection/classification problems. However, the network complexities of
large-scale classifiers present them as one of the most challenging and energy
intensive workloads across the computing spectrum. In this paper, we present a
new approach to optimize energy efficiency of object detection tasks using
semantic decomposition to build a hierarchical classification framework. We
observe that certain semantic information like color/texture are common across
various images in real-world datasets for object detection applications. We
exploit these common semantic features to distinguish the objects of interest
from the remaining inputs (non-objects of interest) in a dataset at a lower
computational effort. We propose a 2-stage hierarchical classification
framework, with increasing levels of complexity, wherein the first stage is
trained to recognize the broad representative semantic features relevant to the
object of interest. The first stage rejects the input instances that do not
have the representative features and passes only the relevant instances to the
second stage. Our methodology thus allows us to reject certain information at
lower complexity and utilize the full computational effort of a network only on
a smaller fraction of inputs to perform detection. We use color and texture as
distinctive traits to carry out several experiments for object detection. Our
experiments on the Caltech101/CIFAR10 dataset show that the proposed method
yields 1.93x/1.46x improvement in average energy, respectively, over the
traditional single classifier model.Comment: 10 pages, 13 figures, 3 algorithms, Submitted to IEEE TVLSI(Under
Review