  As one of most fascinating machine learning techniques, deep neural network
(DNN) has demonstrated excellent performance in various intelligent tasks such
as image classification. DNN achieves such performance, to a large extent, by
performing expensive training over huge volumes of training data. To reduce the
data storage and transfer overhead in smart resource-limited Internet-of-Thing
(IoT) systems, effective data compression is a "must-have" feature before
transferring real-time produced dataset for training or classification. While
there have been many well-known image compression approaches (such as JPEG), we
for the first time find that a human-visual based image compression approach
such as JPEG compression is not an optimized solution for DNN systems,
especially with high compression ratios. To this end, we develop an image
compression framework tailored for DNN applications, named "DeepN-JPEG", to
embrace the nature of deep cascaded information process mechanism of DNN
architecture. Extensive experiments, based on "ImageNet" dataset with various
state-of-the-art DNNs, show that "DeepN-JPEG" can achieve ~3.5x higher
compression rate over the popular JPEG solution while maintaining the same
accuracy level for image recognition, demonstrating its great potential of
storage and power efficiency in DNN-based smart IoT system design.
