Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetching
Sujin Kook Won-Yong Shin Seong-Lyun Kim Seung-Woo Ko
The vision of pervasive artificial intelligence (AI) services can be realized
by training an AI model on time using real-time data collected by internet of
things (IoT) devices. To this end, IoT devices require offloading their data to
an edge server in proximity. However, transmitting high-dimensional and
voluminous data from energy-constrained IoT devices poses a significant
challenge. To address this limitation, we propose a novel offloading
architecture, called joint data deepening-and-prefetching (JD2P), which is
feature-by-feature offloading comprising two key techniques. The first one is
data deepening, where each data sample's features are sequentially offloaded in
the order of importance determined by the data embedding technique such as
principle component analysis (PCA). Offloading is terminated once the already
transmitted features are sufficient for accurate data classification, resulting
in a reduction in the amount of transmitted data. The criteria to offload data
are derived for binary and multi-class classifiers, which are designed based on
support vector machine (SVM) and deep neural network (DNN), respectively. The
second one is data prefetching, where some features potentially required in the
future are offloaded in advance, thus achieving high efficiency via precise
prediction and parameter optimization. We evaluate the effectiveness of JD2P
through experiments using the MNIST dataset, and the results demonstrate its
significant reduction in expected energy consumption compared to several
benchmarks without degrading learning accuracy.